research paper about second language learning

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Open access
• Published: 19 June 2020

The social brain of language: grounding second language learning in social interaction

• Ping Li   ORCID: orcid.org/0000-0002-3314-943X 1 &
• Hyeonjeong Jeong   ORCID: orcid.org/0000-0002-5094-5390 2  

npj Science of Learning volume  5 , Article number:  8 ( 2020 ) Cite this article

37k Accesses

40 Citations

39 Altmetric

Metrics details

• Human behaviour

For centuries, adults may have relied on pedagogies that promote rote memory for the learning of foreign languages through word associations and grammar rules. This contrasts sharply with child language learning which unfolds in socially interactive contexts. In this paper, we advocate an approach to study the social brain of language by grounding second language learning in social interaction. Evidence has accumulated from research in child language, education, and cognitive science pointing to the efficacy and significance of social learning. Work from several recent L2 studies also suggests positive brain changes along with enhanced behavioral outcomes as a result of social learning. Here we provide a blueprint for the brain network underlying social L2 learning, enabling the integration of neurocognitive bases with social cognition of second language while combining theories of language and memory with practical implications for the learning and teaching of a new language in adulthood.

Similar content being viewed by others

A methodological perspective on learning in the developing brain

Anna C. K. van Duijvenvoorde, Lucy B. Whitmore, … Kathryn L. Mills

Skills and motivations underlying children’s cumulative cultural learning: case not closed

E. Reindl, A. L. Gwilliams, … C. Tennie

Language combinations of multilinguals are reflected in their first-language knowledge and processing

Olga Kepinska, Jocelyn Caballero, … Fumiko Hoeft

The study of the neuroscience of cognition has made great strides in the last two decades, thanks to the rapid developments in non-invasive neuroimaging techniques and the corresponding data analytics. At the same time, the study of language acquisition, including second language (L2) learning by children and adults, has also progressed significantly from behavioral research toward neurocognitive understanding, thanks also to new methods including neuroimaging. These two domains of study (i.e., cognitive neuroscience and language learning) have seen increasingly happy marriages of approaches, theories, and methodologies in the last two decades, driven largely by the New Science of Learning 1 , a framework for studying learning at the intersection of psychology, neuroscience, education, and machine learning. Specifically, this framework argues that learning should be studied along three important dimensions: a computational process, a social process, and a process supported by brain circuits linking perception and action. Meltzoff and colleagues 1 further suggested that human language acquisition provides a bona fide example for connecting computational learning, social learning, and brain circuits for perception and action. Despite the call from this multi-disciplinary perspective, researchers in cognitive neuroscience and language acquisition have remained to focus on the individual learner, especially in the study of adult L2 learning. This tradition has seriously limited our understanding of a key aspect of what it means to learn: learning in the social context, interactively.

The study of language learning focused on the individual might have had its origin in the tradition of generative linguistics 2 , 3 , according to which linguistics as a science should study the language competence of the idealized speaker and the corresponding innate mechanisms that enable humans to learn language. Although the neuroscience of language has largely avoided accepting the generative tradition, the focus on the individual, and consequently, the brain structure and function of the individual (i.e., the “single-brain” approach 4 ), has not changed as a field (see Fig. 1 for illustration ) . This is unfortunate, since language serves a social communicative purpose and is fundamentally a social behavior. Note that there are some exceptions to this focus, especially in (a) the study of child language learning (see discussion next), and (b) social neuroscience, which has begun to focus on how brains respond to social interactions using methodologies such as hyper-scanning 4 , 5 . In addition, although leading models of the neurobiology of language do not incorporate a social component 6 , there have been recent efforts to extend the landscape to include pragmatic reasoning 7 , theory of mind 8 , and social interaction 9 .

Left: Traditional approaches for “single-brain” study of language learning; Right: “Social-interactive brain” research and emerging methods.

In this paper, we advocate an approach focused on grounding L2 learning in social interaction; we call this approach “Social L2 Learning” (SL2). Specifically, we define “social interaction” here as “learning through real-life or simulated real-life environments where learners can interact with objects and people, perform actions, receive, use, and integrate perceptual, visuospatial, and other sensorimotor information, which enables learning and communication to become embodied.” Notwithstanding generative linguistics and individual-brain study approaches, the field of first language (L1) acquisition has clearly demonstrated that children, from the earliest stages, depend on social interactions to learn 1 . This dependence may be initially coordinated through “joint attention” and shared intentionality between the infant and the parent/caregiver 10 . Computational models that incorporate social-interactive cues from mother–child interactions perform significantly better than models with no such cues included 11 , 12 . Kuhl et al. 13 further indicated that social learning is crucial even when children learn an L2: American babies exposed to Mandarin Chinese through a “DVD condition” (pre-recorded audiovisual or audio-only material) did not demonstrate learning of Mandarin phonetic categories as did babies who were exposed to the same material through a “live condition” (experimenter interacting with the infant during learning). For adults, however, folk wisdom suggests that they can learn an L2 rapidly without social cues (e.g., through intensive training in a classroom) and may be less dependent on the presence of peer learners. Limited evidence, however, suggests that social cues such as joint attention may also enhance L2 learning success through orienting the learner’s attention to the correct meaning among competing alternatives 14 .

Theoretical frameworks for understanding the social brain of language Learning

The proposed SL2 model is focused on grounding L2 learning in social interaction based on both behavioral and brain data. A number of important theoretical framworks have already paved the way for the SL2 model, some of which are separately known in the domains of psycholinguistics, memory, and cognition, respectively.

First, while the classic Critical Period Hypothesis 15 suggests a biology-based account of effects of age of acquisition (AoA) on learning, the Competition Model, in its various formulations 16 , 17 , 18 , 19 , 20 , provides a social-based and interactive-emergentist account of the differences between L1 and L2 learning. Upon this account, the principles of learning are not fundamentally different between the child learning an L1 and the adult learning an L2 (e.g., contra the “less is more” hypothesis 21 ), but the processes and contexts within which learning takes place may be significantly different. For children, language learning is a natural event that unfolds in the environment where they grow up. They can naturally integrate the rich perceptual and sensorimotor experiences from this environment, interacting with the objects and people and performing actions in it. Picking up and using a spoon while hearing the sound “spoon” is part of the learning process, which differs from the process where adults sitting in the classroom look at a picture of spoon and associate it to an existing label in their native language. According to MacWhinney 17 , adult L2 learning is susceptible to several major “risk factors”, factors that prevent adults from acquiring a foreign language to native competence. These include thinking in L1 only (which implies the need to translate from L2 to L1 rather than directly using L2 as a medium), social isolation (learning as an individual or through in-group communities only), and lack of perception-action resonance (lack of direct contact with the target objects or actions in the environment while learning L2). These risk factors, particularly social isolation and lack of perception-action-based contexts, may explain why adult learners display the strong parasitic L2-on-L1 representations 22 : on the one hand, adults typically start to learn L2 when they have already established a solid L1 (“entrenchment” in L1), which lends easily to L2-to-L1 translation and association; on the other hand, they lack a dynamic and variable environment to build direct relations between L2 words and the objects/concepts to which the words refer 23 . With regard to the risk factors of thinking in L1 and social isolation, empirical evidence has shown that study-abroad experience may provide some environmental support, particularly in attenuating L1 to L2 interference for late adult learners 24 .

These theoretical perspectives are consistent with a larger trend in psycholinguistics to examine language learning and bilingualism not as an individualized but a general communicative experience. Adults show significant differences in how they learn two (or more) languages, the frequency and contexts with which they use the languages, and the communicative purposes for which each language is needed, therefore showing that bilingualism is a highly dynamic developmental process 19 , 25 , 26 , 27 . The SL2 approach advocated here also echoes a movement in the broader language science, from sociocultural theory 28 to usage-based language learning 29 and conversational analysis 30 , all of which view language learning as a socially grounded process. Ellis 31 summarizes this movement with regard to its focus on “how language is learned from the participatory experience of processing language during embodied interaction in social and cultural contexts where individually desired outcomes are goals to be achieved by communicating intentions, concepts, and meaning with others.”

Second and independently, human memory research suggests that item-based learning (encoding) and use (retrieval) are highly interdependent. This is due to the associative nature of memory, in which the cognitive operations used for encoding stimulus items directly impact their subsequent retrieval. A well-established hypothesis in this regard is the “encoding-specificity” principle 32 , according to which semantic memories are more successfully retrieved if they are recalled in the same context as when they were originally encoded (e.g., if word lists were encoded underwater they would be recalled better underwater than on dry land 33 ). Related to this hypothesis is the “levels of processing” theory 34 that suggests deeper, more elaborative, or richer semantic processing during encoding would lead to more successful retrieval than shallow or surface-level processing of the same material. If encoding involves more elaborative semantic processing, e.g., using multimodal information, it will have a positive impact on memory retention and retrieval. Both the “dual encoding” theory 35 and the multimedia learning theories 36 suggest that elaborative processing with multimodal sensory information could enhance the quality of semantic memory, hence leading to better recall. One of the predictions here is a “multimodal advantage” such that, for example, people learn better with words and pictures together than with words alone 37 .

There have been several studies that build on the encoding-specificity principle to account for bilingual language processing. Marian and Kaushanskaya 38 proposed a language-dependent memory hypothesis to explain bilingual semantic/conceptual representation, according to which language is encoded in the episodic memory of an event and therefore forms part of one’s autobiographical memory. It is this episodic encoding that influences the accessibility of semantic memories. They observed that memories were more accessible when retrieved in the same language in which they were originally encoded or learned. Furthermore, this language specificity in bilingual memory is influenced by variables such as AoA, proficiency in the L2, and history of usage in the two languages 39 , 40 ; for example, richer memories were associated with an earlier age of L2 learning.

The richness of memory with regard to AoA may be explained by the rich episodic experiences/events associated with specific perceptual-sensory features in the environments, perhaps because early L1 learning includes these experiences but late L2 learning typically does not. This leads us to the embodied cognition theory 41 , 42 , according to which body-specific (e.g., head, hand, foot) and modality-specific (e.g., auditory, visual, tactile) experiences form an integral part of the learner’s mental representation of concepts, objects, and actions. This contrasts with classic cognitive theories of symbolic representation that argue that cognition and cognitive operations are modular, and that language is unrelated to the rest of cognition including perception and action 43 , 44 . The embodied cognition theory highlights the whole-body interaction with the context, that is, “interaction between perception, action, the body and the environment” 45 , and when engaged, will also activate the brain’s perceptual and sensorimotor cortex 46 , 47 . Although the embodied cognition hypotheses have been examined in many studies of brain and behavior, so far, the focus has been on native L1 speakers; whether and how body-specific and modality-specific experiences play the same role in L2 learning has not received much attention 48 , 49 . Our SL2 model argues for the important role of social interaction for L2 learning and draws on the link between learning and perception and action, as suggested by the New Science of Learning framework 1 .

Social interaction for second language learning: neuroimaging evidence

How do the theoretical frameworks above shed light on our SL2 approach in understanding the social brain of L2 learning? Although many recent neuroimaging studies have examined brain changes resulting from L2 learning 50 , most of this literature has focused on traditional L2 learning methods such as rote memorization or translation-based learning, in either classroom settings or lab-based intensive training 51 , 52 , 53 , 54 , 55 . Their findings suggest largely the engagement of language-related neural networks (e.g., the classic frontal-parietal network) and memory-related brain regions (e.g., the medial temporal region for the learning and consolidation of linguistic information; see Fig. 1 for illustration). So far, only a handful of studies have provided initial evidence on the neural networks implicated in social-based L2 learning, pointing to the following key patterns.

First, the supramarginal gyrus (SMG) and the angular gyrus (AG) could play a significant role. In one of the first studies in this domain, Jeong et al. 56 trained Japanese speakers to learn Korean words under two conditions, either through L1 translation or simulated social interaction in which the participants watched videos that showed joint activities in real-life situations (e.g., the L2 target word “Dowajo”, meaning help me in English, is shown in the video with an actor trying to move a heavy bag and asking another actor for help). The authors then asked participants to retrieve the target L2 words in a functional magnetic resonance imaging (fMRI) session. The results indicated that the words learned through videos with social interactions produced more activation in the right SMG whereas the words learned from translation produced more activity in the left middle frontal gyrus (MFG). Interestingly, retrieval of L1 words (acquired by these participants in childhood through daily life) also produced greater activation in the right SMG. These findings can be interpreted to suggest that L2 words learned via social interaction (as simulated in videos through short-term training) are processed in a similar fashion as L1 words.

Second, the right inferior parietal cortex (IPL, including both SMG and AG) has been implicated more strongly in virtual reality-based (VR) interactive learning as compared with non-virtual, word-to-picture association, learning 57 . Legault and colleagues found that cortical thickness, a structural brain measure of gray-matter thickness from the surface of the cortex to the white matter, is associated with different contexts of learning: after 2–3 weeks of intensive L2 vocabulary training across seven sessions, the VR learners showed a positive correlation in the right IPL with performance across all training sessions, while the non-VR learners showed a positive correlation at the final stages only in the right inferior frontal gyrus (IFG), a region associated with effective explicit language training 58 (though there is counter evidence 59 ). Furthermore, cortical thickness in the right SMG was correlated with higher accuracy scores of the delayed retention test, but only for the VR learning group. The VR group was engaged in 3D virtual environments in which the learners could dynamically view or play with the objects in an interactive manner.

Third, the right SMG is shown to be more activated in simulated partner-based learning than individual-based learning of word meanings, indicating that the mere presence of a social partner would facilitate L2 word learning 59 , like in child language learning. Verga and Kotz 59 further found that participants with higher learning outcomes showed higher activity in the right IFG during an interactive learning condition but not during an individualized non-interactive learning condition. Levels of activity in the right lingual gyrus (LG) and right caudate nucleus (CN), previously implicated in visual search process and visuospatial learning, were also found to correlate with temporal coordination between a learner and a partner during simulated interactive learning.

These brain imaging data suggest that social-based L2 learning versus classroom-based individual learning conditions can lead to distinct neural correlates; for example, social learning of L2 may engage more strongly the brain regions for visual and spatial processing 57 , 59 , which may have consequences on both encoding (learning) and retrieval of information (memory). In contrast to the idea that only the child brain may respond to social learning, these findings suggest that the adult brain displays significant neuroplasticity in response to social interaction. Jeong et al. 56 showed that if an L2 word was initially encoded in a more socially interactive condition (through video simulations), it engaged the relevant brain areas as in L1, areas that would not become activated if learning had occurred through word association or translation as in a typical L2 classroom.

Figure 2 illustrates the proposed neural correlates of social interaction in the frontal, parietal, and subcortical regions for L2 learning. The strong engagement of the SMG, AG, IFG, along with the visual (LG) and subcortical regions (CN), may form an important neural network for understanding how SL2 is instantiated in the human brain. Importantly, this network highlights the stronger role of the right-hemisphere brain regions as compared with the typical left-lateralized language networks. The IFG has long been implicated in lexical-semantic processing and its integration with memory 60 , which is shown bilaterally in both hemispheres in Fig. 2 . The other regions, the SMG, AG, LG, CN, are illustrated in Fig. 2 on the right hemisphere. The role of this “right-heavy” network is evidence of the significant neurocognitive impacts of social L2 learning as opposed to traditional methods (Fig. 1 ).

The left hemisphere regions (blue) handle lexical-semantic processing, while the right hemisphere cortical plus the subcortical regions (green) participate in social learning. IFG inferior frontal gyrus, SMG supramarginal gyrus, AG angular gyrus, LG lingual gyrus, CN caudate nucleus, MTG/ITG middle temporal gyrus/inferior temporal gyrus (Note: the right hemisphere is depicted on the left side and left hemisphere on the right side).

There are a number of important issues for further consideration with regard to the SL2 network charted in Fig. 2 . First, it is important to understand how the various areas collaborate and communicate with each other during learning and memory. A true brain network is one that involves modules, communities, and pathways that are dynamically connected and organized. An important research direction in neuroscience today is the network science approach towards the analysis of functional/structural brain patterns underlying cognition, and significant advances have been made in applying this approach to the understanding of neural circuits of learning and memory, including L2 learning 61 , 62 , 63 . It remains to be understood how the left frontal IFG and right parietal IPL areas (including SMG and AG) form a dynamic network in support of SL2 learning, alongside the visual and subcortical regions (LG and CN). It is possible that the LG and CN regions play an important early role in visuospatial analysis and learning in social settings, which feeds into action-based lexico-semantic and conceptual integration that heavily involves the SMG and AG regions, as evidenced in studies by Verga and Kotz 59 , Jeong et al. 56 , and Legault et al. 48 . The IFG then coordinates this network with significant participation of semantic memory and cognitive control as well as lexical retrieval 64 . In this regard, the IFG also plays a significant role in modulating competition between L1 and L2 in a language control network 19 , 65 .

Second, a related issue for further study is how such neural networks evolve during development, which would allow us to understand the degree to which time of learning (e.g., AoA), extent of learning, and increased proficiency may impact the dynamic changes in the neural network 50 . Elsewhere significant progress has been made in this domain 20 , 66 , 67 , but the focus there has been on the relationship between cognitive control and bilingualism and the related debate on bilingual cognitive advantage (see a recent discussion 25 ). Methodologically, to study the developmental process we will also need to pursue longitudinal neuroimaging work 51 as well as short-term intensive training paradigms. Finally, much work is needed for understanding how the SL2 network may overlap with neural networks implicated in other types of social interaction 68 , 69 . Hagoort and Indefrey 7 , 70 suggested that pragmatic inference in language processing involves the “theory of mind” (ToM) or the mentalizing network 71 , 72 , in which the medial prefrontal (mPFC), along with the temporoparietal junction (TPJ) regions, play an important role in social reasoning such as thinking about other people’s beliefs, emotions, and intentions. Not surprisingly, the extended language network (ELN) hypothesis for narrative text comprehension 73 significantly overlaps with the ToM network, involving mPFC and the TPJ in building story coherence, drawing inference, and interpreting pragmatic meaning in the narrative story being read. The ELN network allows the reader to follow the plots, empathize with the characters, and take the protagonist’s perspectives 74 , 75 . We hypothesize that the SL2 network in Fig. 2 dynamically connects to mPFC and TPJ implicated in ToM and social reasoning, although this hypothesis needs to be examined carefully by comparing learning with social interaction versus without.

New approaches toward SL2 as a theoretical hypothesis and a practical model

Embodied semantic representation in l1 and l2.

In a typical adult L2 learning setting, students rely on translation/association of two languages and rote memory, unlike the child who acquires the L1 with sensorimotor experiences in an enriched perceptual environment. For example, in an L2 classroom, the teacher introduces a new L2 word (e.g., Japanese “inu”) by its translation equivalent in the L1 (e.g., English “dog”) and the learner’s task is to form paired associations between L1 and L2 when learning the L2 vocabulary. Although this method is efficient early on, it leads to what is called a parasitic lexical representation: the L2 word is conveniently linked to a conceptual system already established through the L1 19 , 22 . Because the task of word association or translation does not encourage direct L2-to-concept relations, the link from the L2 word to the concept is weak, and has to be indirectly mediated via the L1-to-concept link 23 . More significantly from the SL2 perspective is the “collateral damage” of this parasitism: the new L2 representation lacks the relevant perceptual-spatial-sensorimotor features (e.g., shape, size, motion and location of “inu” or dog), features that are an integral part of the lexical-semantic representation in the L1.

Why can’t the adult L2 learner take the newly acquired L2 representation and map it to the rich embodied features in the L1 representational system, given that would be the most efficient way? Several computational models 22 , 76 , 77 have systematically manipulated the timing of adding new L2 items to L1 lexical structure during simultaneous or sequential learning of the two languages and showed that the L2 lexical organization is sensitive to AoA: the later L2 is learned, the less well organized and more fragmented the L2 representations are. Thus, parasitism is characteristic of L2 semantic learning in late adulthood. Hernandez et al. 19 and Li 78 attributed this to the mechanism of “entrenchment”, in which the lexical structure established by the L1 early on is entrenched to resist radical changes during later L2 learning. The entrenchment may have led to late adult L2 learner’s inability to map L2 forms directly to the rich L1 lexico-semantic representations. In a recent neuroimaging study comparing L1 vs. L2 embodied semantic representations, Zhang et al. 79 showed that L1 speakers engage a more integrated brain network connecting key areas for language and sensorimotor integration during lexico-semantic processing, whereas L2 speakers fail to activate the necessary sensorimotor information, recruiting a less integrated embodied brain system for the same task.

The persistent parasitism could also be attributed to the different contexts in which the two languages have been learned. Recent evidence from affective processing indicates that affective-specific experiences are more strongly evoked in L1 than in L2 words due to the different contexts of social learning (e.g., family vs. workplace interactions) and the co-evolution of emotional regulation systems with early language systems 80 , 81 , 82 . Consistent with embodied semantic differences 79 , such emotionality differences between L1 and L2 have been found most reliable when the L2 is a later-learned or less proficient language 80 , showing evidence that the L2 representation, if acquired late, cannot easily incorporate the rich social and affective features of the L1 representation.

How can the L2 learner break away from this parasitism so as to establish the L2 representation on a par with the L1 representation? SL2 provides a theoretical framework for addressing this question from an embodied cognition perspective. Recent work suggests that embodied actions, even when no direct social interaction is involved, can impact learning outcomes simply by engaging the body, for example, through gestures. Mayer et al. 49 showed neurocognitive differences between (a) L2 vocabulary learning with gestures that activated the superior temporal sulcus, STS, and the premotor areas, versus (b) learning without gestures that activated the right lateral occipital cortex only. Critically, learners in the gesture condition showed significantly better memory for L2 words, hence more sustained retention, than the non-gesture learners, even after 2–6 months. Such findings point to the significance of embodied “body-specific” (hands in this case) activities for learning, and are consistent with the sensorimotor-based neural accounts of semantic representation 20 . According to the “hub-and-spoke model” 83 , 84 , “modality-specific” versus “modality-independent” (or “amodal”) representations are realized in different neural circuits, in visual/auditory/motor areas versus anterior temporal lobe, respectively. However, the outcome conceptual system must encode knowledge through integrating higher-order relationships among sensory, motor, affect, and language experiences. In this regard, one of the outstanding questions raised by Pulvermüller 84 was whether semantic learning from embodied experience and context could lead to different semantic representations in the mind and the brain. This question becomes particularly relevant when we examine the contexts of SL2 learning.

Simulated social interaction, technology, and the brain

In addition to the cognitive and neuroscience models that support SL2 theoretically, recent advances in technology have enabled us to study SL2 as a practical model toward building embodied representations in the L2 through technology-based learning. Because of the L1 vs. L2 embodied representation differences 79 , the L2 learner should aim at integrating modality-specific information with the newly acquired L2 amodal representations, in order to fully approach native-like conceptual-semantic representations. Technology-based learning could aid in this process from the earliest stages of learning, given the ample evidence from (a) technology-enhanced child language learning 85 , 86 , (b) prevalence of technology-based multimedia learning for both children and adults 36 , and (c) evidence of multimedia learning effects on the brain 37 . For example, in child language, despite a clear advantage of live learning compared to screen-based DVD learning 13 , it is now shown that direct face-to-face human interaction is not a necessary condition for infant foreign language learning. Children can benefit from technology such as Skype and other screen media platforms, provided that these technologies can deliver simulated social interactions, for example, through video chats 85 . Lytle et al. 86 showed that when the same learning materials from Kuhl et al. 13 were delivered to children through play sessions with an interactive touchscreen video, children can indeed learn from the videos. This study clearly points to both the role of interactive social play (simulated through touchscreen videos) and the impact of technology, breaking the simple dichotomy between live human learning (as effective) vs. screen-based learning (as ineffective).

In real-life learning situations, students observe and integrate multiple sources of information including actions and intentions of the speaker for using specific words in specific contexts. In a follow-up study of Jeong et al. 56 , Jeong et al. 87 examined fMRI evidence during learning (i.e., encoding), under both traditional translation and simulated social interaction conditions. The authors controlled for the amount of visual information in the two conditions by using L1 text and L1 videos as baseline comparisons. In the simulated video condition, participants had to infer the meaning of L2 target words by observing social interactions of others. Learning of L2 words in this condition resulted in additional activation in the bilateral posterior STS and right IPL. Compared with learning through L1 translation, this condition also resulted in significant positive correlations between performance scores at delayed post-test and neural activities in the right TPJ, hippocampus, and motor areas.

Jeong et al.’s new findings showed that simulated social interaction methods, compared with traditional translation/association methods, may result in stronger neural activities in key brain regions implicated for memory, perception and action, which can boost both recall and sustained long-term retention. These results are consistent with the semantic memory encoding and retrieval theories reviewed earlier. They are also consistent with recent multimedia learning effects on the brain, reflected in the bimodal encoding advantage that materials learned in multimodal conditions (e.g., learned from videos that engage both auditory and visual channels 37 ) may lead to sustained neural activities in AG, mPFC, hippocampus, posterior cingulate, and subcortical areas. These brain areas, including mPFC, TPJ, and hippocampus, significantly overlapped with the SL2 brain network and the ToM network that relies on social learning and reasoning (Fig. 2 ).

Videos or other multimedia platforms, although very effective as discussed, nevertheless have their limits with regard to social interaction and “whole-body” embodiment/engagement as in real life. Recent technological advances in immersive technologies (e.g., virtual reality, VR and augmented reality, AR) enable social interaction to a greater extent, by simulating real-world contexts and promoting student learning through active and self-exploratory discovery processes 88 . VR also provides a new platform to connect cognition, language learning, and social interaction, as it allows researchers to simulate the process of learning in its natural ecology without sacrificing experimental rigor 89 , 90 . In the current consideration, and in light of Competition Model and Embodied Cognition theories discussed, VR provides a tool for students to learn L2 in a new way. Specifically, it enables the adult learner, like the child L1 learner, to directly map (“perceptually ground”) the L2 material during learning onto objects, actions, and episodic memory to form embodied semantic representations in the L2.

Although VR has been applied to L2 teaching and learning, systematic and experimental research is still scarce in understanding the effects of VR as a function of both features of the technology and characteristics of the learner 90 . Lan et al. 91 and Hsiao et al. 92 provided early evidence in this regard. The authors trained American students to learn Mandarin Chinese vocabulary through Second Life , a popular desktop virtual platform of gaming and social networking, and demonstrated that (a) the virtual learners needed only about half of the number of exposures to gain the same level of performance as learners through traditional associative learning, and (b) virtual learners showed faster acceleration of later-stage learning. More importantly, clear individual differences in learning were observed: the low-achieving learners tended to follow a fixed route in the virtual space (using the “nearest neighbor” strategy to learn), whereas the high-achieving learners were more exploratory, grouping together similarly sounding words or similarly looking objects for learning. Interestingly, such individual difference patterns could be captured by statistical methods such as “roaming entropy” to quantify the degree or variability of movement trajectories in self-directed exploration of space, a measure previously shown to correlate with neural development during spatial navigation 93 : better learners showed higher roaming entropy, indicating more exploratory analyses of the virtual environment. Thus, navigation patterns in the VR may reflect how learners conceptually organize the environment and their abilities to explore it interactively.

Most L2 virtual learning studies, like Lan et al. 91 , have relied on desktop virtual platforms like Second Life rather than more interactive and immersive VR (iVR). Limited evidence suggests that iVR, with its more realistic simulation of the visuospatial environment and more bodily activity and interaction, leads to higher accuracy in memory recall tasks 94 . It is likely that iVR, compared to desktop VR, more strongly engages the perceptual-motor systems and maximizes the integration of modality-specific experience, and therefore generates better embodied representation 90 . In Legault et al. 48 , participants wore head-mounted displays to view and interact with objects/animals in an iVR kitchen or zoo, and showed significantly better performance of L2 vocabulary attainment than learning through the L2-to-L1 word-to-word association method. Further, the kitchen words were learned better than the animal words, presumably because the learner could more directly manipulate the virtual objects in the kitchen (e.g., squatting and picking up a broom and moving it around; see Fig. 3 for an illustration) than they could with the virtual animals in the zoo. The iVR kitchen environment thus conferred more “whole-body” interactive experience to the learner, especially with respect to the engagement of the sensorimotor system 95 .

a In the iVR kitchen, the learner used her handset to point to any item and hear the corresponding word (e.g., “dao”, Chinese knife in the example). b The learner could pick up and move objects (broom in the example) by pressing a trigger button with index finger; ( c ) position of the learner picking up the item (broom)—the learner consented to the use of her photo here. d Left panel: Effect of learning context (iVR vs. word-word association); Right panel: effect of category of learning (iVR kitchen vs. iVR zoo). Error bars indicate 95% confidence intervals (CIs). * indicates significant effect (from Legault et al. 48 ; copyright permission from MDPI).

In terms of the SL2 framework, VR has the promise of providing a context of learning for children and adults on equal footing, and in particular, it simulates “situated learning”, a condition whereby learning takes place through real-world experiences and visuospatial analyses of the learning environment, experiences and analyses that are often absent in a typical classroom 88 . Therefore, the positive benefits of SL2 learning based on either real or simulated social interactions are clear, including at least the aforementioned aspects of (a) embodied, native-like, neural representation 56 , (b) more sustained long-term memory 49 , and (c) less susceptibility to L1 interference 24 . These benefits not only apply to foreign language learning, but also other educational contents such as spatial learning and memory 90 and learning of subjects in STEM (i.e., science, technology, engineering, and mathematics) 88 .

VR is an excellent example of the power of today’s technology-based learning, and it urges us to study how students can take advantage of rapidly developing technologies for better learning outcomes. We need to pay attention to the specific key features that support VR learning (e.g., immersive experience, spatial navigation, and user interactivity), the individual differences therein (e.g., cognitive characteristics of the learner including memory and motivation), and the underlying neurocognitive mechanisms (e.g., sensorimotor integration) that enable VR as an effective tool 90 . In this regard, the SL2 approach we advocate here will have the potential of not only benefitting students in terms of reaching native-like linguistic representation and communicative competence, but also providing specific recommendations to teachers in the classroom, especially for those struggling students who may need help in integrating multiple sources of information through contextualized learning. For example, as indicated by Legault et al. 48 , it is the struggling students (“the less successful learners”) who benefitted more from VR learning than from non-VR learning, whereas for the successful learners, VR versus non-VR learning did not make a significant difference. Consistent with the larger trend in education to promote personalized learning and active learning in STEM 96 , there is a movement for today’s classroom instructions to be structured differently from the traditional “teacher-centered” instructional methods, to encourage more “student-centered” interactions and in-depth discussions (e.g., the “flipped classroom” model). E-learning technologies including VR play a significant role in this movement.

Future directions

New exciting research in the neurocognitive mechanisms of SL2 has just begun. To understand different aspects of L2 learning from a multi-level language systems and multiple networks perspective 7 , neuroimaging studies should extend their focus from the lexico-semantic level to phonological, morphological, syntactic, and discourse levels with the SL2 approach. For example, if, as in infant L1 learning, L2 phonology can be learned through socially enriched linguistic exposure (e.g., multi-talker variability, visible articulation), then even late L2 adult learners may advance to native competence 97 . It is also important to examine how social interaction impacts the acquisition of different types of syntactic rules (e.g., cross-linguistically different syntactic features), as demonstrated in a recent fMRI study of the acquisition of possessive constructions in Japanese Sign Language 98 . The relationship between lexical versus syntactic acquisition is also a topic of significant research interest. While lexical learning typically elicits stronger involvement of the declarative system, morphosyntactic learning likely involves to a greater extent the procedural memory system 99 , 100 . How L2 lexical learning may also engage the procedural memory system in light of the SL2 brain network (Fig. 2 ) needs to be seriously considered and carefully examined in future studies.

Despite the significant effects of social L2 learning, individual differences have been observed as discussed 48 , 92 . It is therefore important to examine in greater detail both the contexts of learning and the characteristics of the learner 90 . Specifically, the magnitude of the effects might depend on the interaction between features of social learning and the learner’s cognitive and linguistic abilities. It is possible that highly interactive, embodied experiences are more helpful to some than to others 48 : learners who are poor at abstract associative learning may benefit more from social-interactive learning. A challenge to future research will be to identify the nature of the interaction between the individual learner’s inherent abilities and the richness of the social learning context.

Finally, a number of new directions present further research opportunities. For example, systematic investigation is needed for understanding the role of various types of non-verbal information that may contribute to positive L2 learning outcomes. Previous cognitive neuroscience studies have provided empirical evidence that non-verbal information (e.g. gesture, communicative intention) facilitates speech comprehension and production, as well as language learning in children and adults 49 , 101 , 102 . Furthermore, it is important to study how SL2 facilitates affective processing such as emotion and motivation 81 , 103 and consequently how it engages the brain’s limbic and subcortical reward systems. As discussed earlier, there is evidence that emotional responses are more strongly associated with L1 than L2 and social contexts may be a significant contributor to this association 80 , 81 . Indeed, social interaction has been studied as one of the most crucial contributors to the development of learning motivation in L2 acquisition 104 , 105 . The SL2 approach provides a framework for integrating previous findings and hypotheses with new insights from affective and cognitive neuroscience to fully understand the social brain of language learning.

Meltzoff, A., Kuhl, P., Movellan, J. & Sejnowski, T. Foundations for a new science of learning. Science 325 , 284–288 (2009).

CAS   PubMed   PubMed Central   Google Scholar  

Chomsky, N. Syntactic structures . Janua Linguarum 4 (The Hague, Mouton, 1957).

Chomsky, N. Aspects of the Theory of Syntax (MIT, Cambridge, MA, 1965).

Redcay, E. & Schilbach, L. Using second-person neuroscience to elucidate the mechanisms of social interaction. Nat. Rev. Neurosci. 20 , 495–505 (2019).

Babiloni, F. & Astolfi, L. Social neuroscience and hyperscanning techniques: past, present and future. Neurosci. Biobehav. Rev. 44 , 76–93 (2014).

PubMed   Google Scholar  

Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8 , 393–402 (2007).

CAS   PubMed   Google Scholar  

Hagoort, P. The neurobiology of language beyond single-word processing. Science 366 , 55–58 (2019).

Ferstl, E. C. Neuroimaging of text comprehension: Where are we now? Ital. j. linguist. 22 , 61–88 (2010).

Google Scholar  

Verga, L. & Kotz, S. A. How relevant is social interaction in second language learning? Front. Hum. Neurosci. 7 , 550 (2013).

PubMed   PubMed Central   Google Scholar  

Tomasello, M. The social-pragmatic theory of word learning. Pragmatics 10 (4), 401–413 (2000).

Yu, C. & Ballard, D. A unified model of early word learning: Integrating statistical and social cues. Neurocomputing 70 (13-15), 2149–2165 (2007).

Li, P., & Zhao, X. In Research Methods in Psycholinguistics and the Neurobiology of Language: A Practical Guide (eds. de Groot, A. & Hagoort, P.) 208–229 (Malden, MA: John Wiley & Sons, Inc., 2017).

Kuhl, P., Tsao, F. M. & Liu, H. M. Foreign-language experience in infancy: effects of short-term exposure and social interaction on phonetic learning. PNAS 100 (15), 9096–9101 (2003).

Verga, L. & Kotz, S. A. Help me if I can’t: Social interaction effects in adult contextual word learning. Cognition 168 , 76–90 (2017).

Lenneberg, E. H. Biological Foundations of Language (John Wiley & Sons, New York, 1967).

Bates, E., & MacWhinney, B. In Mechanisms of Language Acquisition (ed. MacWhinney, B.) 157–194 (Lawrence Erlbaum Associates, 1987).

MacWhinney, B. The Routledge Handbook of Second Language Acquisition (eds. Gass, S. & Mackey, A.) 211–227 (New York: Routledge, 2012).

Li, P., & MacWhinney, B. In The Encyclopedia of Applied Linguistics (ed. Chapelle, C. A.) 1–5 (John Wiley & Sons, Inc., Malden, MA, 2013).

Hernandez, A., Li, P. & MacWhinney, B. The emergence of competing modules in bilingualism. Trends Cogn. Sci. 9 , 220–5 (2005).

Hernandez, A. & Li, P. Age of acquisition: its neural and computational mechanisms. Psychol. Bull. 133 (4), 638–650 (2007).

Johnson, J. S. & Newport, E. L. Critical period effects in second language learning: the influence of maturational state on the acquisition of English as a second language. Cogn. Psychol. 21 , 60–99 (1989).

Zhao, X. & Li, P. Bilingual lexical interactions in an unsupervised neural network model. IJB 13 , 505–524 (2010).

Kroll, J. F. & Stewart, E. Category interference in translation and picture naming: evidence for asymmetric connections between bilingual memory representations. J. Mem. Lang. 33 , 149–174 (1994).

Linck, J., Kroll, J. & Sunderman, G. Losing access to the native language while immersed in a second language: Evidence for the role of inhibition in second-language learning. Psychol. Sci. 20 (12), 1507–1515 (2009).

DeLuca, V., Rothman, J., Bialystok, E. & Pliatsikas, C. Redefining bilingualism as a spectrum of experiences that differentially affects brain structure and function. PNAS 116 (15), 7565–7574 (2019).

Grosjean, F. In The Psycholinguistics of Bilingualism (eds. Grosjean, F. & Li, P.) 5–25 (Wiley & Sons, Inc., Malden, MA, 2013).

Li, P. In The Handbook of Language Emergence (eds. MacWhinney, B., & O’Grady, W.) 511–536 (John Wiley & Sons, Inc., Maiden, MA, 2015).

Lantolf, J. Sociocultural theory and L2: State of the art. Stud. Second Lang. Acquis. 28 , 67–109 (2006).

Tomasello, M. Constructing a Language: A Usage-based Theory of Language Acquisition (Harvard University Press, Cambridge, 2003).

Hall, J. K. The contributions of conversation analysis and interactional linguistics to a usagebased understanding of language: expanding the transdisciplinary framework. Mod. Lang. J. 103 , 80–94 (2019).

Ellis, N. C. Essentials of a theory of language cognition. Mod. Lang. J. 103 , 39–60 (2019).

Tulving, E. & Thomson, D. M. Encoding specificity and retrieval processes in episodic memory. Psychol. Rev. 80 (5), 352–373 (1973).

Godden, G. & Baddeley, A. Context-dependent memory in two natural environments: on land and underwater. Br. J. Psychol. 6 , 355–369 (1975).

Craik, F. I. & Lockhart, R. S. Levels of processing: a framework for memory research. J. Verbal Learning Verbal Behav. 11 (6), 671–684 (1972).

Paivio, A. Mental Representations: A Dual Coding Approach . (Oxford University Press, Oxford, 1990).

Mayer, R. E., Moreno, R., Boire, M. & Vagge, S. Maximizing constructivist learning from multimedia communications by minimizing cognitive load. J. Educ. Psychol. 91 (4), 638–643 (1999).

Liu, C., Wang, R., Li, L., Ding, G., Yang, J. & Li, P. Effects of encoding modes on memory of naturalistic events. J. Neurolinguist. 53 , 100863 (2020).

Marian, V. & Kaushanskaya, M. in Relations Between Language and Memory : Sabest Saarbrucker Beitrage zur Sprach- und Transl (ed. Zelinsky-Wibbelt, C.) 95–120 (Frankfrut, Peter Lang, 2011).

Marian, V. & Neisser, U. Language-dependent recall of autobiographical memories. J. Exp. Psychol. Gen. 129 (3), 361–368 (2000).

Marian, V. & Kaushanskaya, M. Language context guides memory content. Psychon. Bull. Rev. 14 (5), 925–933 (2007).

Barsalou, L. W., Niedenthal, P. M., Barbey, A. K. & Ruppert, J. A. In The Psychology of Learning and Motivation: Advances in Research and Theory (ed. Ross, B. H.) 43–92 (Elsevier Science, 2003).

Glenberg, A. M., Sato, M. & Cattaneo, L. Use-induced motor plasticity affects the processing of abstract and concrete language. Curr. Biol. 18 , R290–R291 (2008).

Chomsky, N. Lectures on Government and Binding (Foris Publications, Dordrecht, Holland, & Cinnaminson, NJ, 1981).

Fodor, J. A. The Modularity of Mind (MIT Press, Cambridge, MA, 1983).

Barsalou, L. W. In Embodied Grounding: Social, Cognitive, Affective, and Neuroscientific Approaches (eds. Semin, G. R. & Smith, E. R.) 9–42 (Cambridge University Press, 2008).

Aziz-Zadeh, L. & Damasio, A. Embodied semantics for actions: findings from functional brain imaging. J. Physiol. Paris 102 (1-3), 35–9 (2008).

Willems, R. M. & Casasanto, D. Flexibility in embodied language understanding. Front. Psychol. 2 , 116 (2011).

Legault, J. et al. Immersive virtual reality as an effective tool for second language vocabulary learning. Languages 4 (1), 13 (2019).

Mayer, K. M., Yildiz, I. B., Macedonia, M. & von Kriegstein, K. Visual and motor cortices differentially support the translation of foreign language words. Curr. Biol. 25 (4), 530–535 (2015).

Li, P., Legault, J. & Litcofsky, K. A. Neuroplasticity as a function of second language learning: anatomical changes in the human brain. Cortex 58 , 301–24 (2014).

Grant, A. M., Fang, S.-Y. Y. & Li, P. Second language lexical development and cognitive control: a longitudinal fMRI study. Brain Lang. 144 , 35–47 (2015).

Qi, Z., Han, M., Garel, K., Chen, E. & Gabrieli, J. White-matter structure in the right hemisphere predicts Mandarin Chinese learning success. J. Neurolinguist. 33 , 14–28 (2015).

Yang, J., Gates, K., Molenaar, P. & Li, P. Neural changes underlying successful second language word learning: An fMRI study. J. Neurolinguist. 33 , 29–49 (2015).

Breitenstein, C. et al. Hippocampus activity differentiates good from poor learners of a novel lexicon. NeuroImage 25 (3), 958–968 (2005).

Tagarelli, K., Shattuck, K., Turkeltaub, P. & Ullman, M. Language learning in the adult brain: a neuroanatomical meta-analysis of lexical and grammatical learning. NeuroImage 193 , 178–200 (2019).

Jeong, H. et al. Learning second language vocabulary: neural dissociation of situation-based learning and text-based learning. NeuroImage 50 (2), 802–9 (2010).

Legault, J., Fang, S., Lan, Y. & Li, P. Structural brain changes as a function of second language vocabulary training: Effects of learning context. Brain Cogn. 134 , 90–102 (2019).

Stein, M., Winkler, C., Kaiser, A. & Dierks, T. Structural brain changes related to bilingualism: does immersion make a difference? Front. Psychol. 5 , 1116 (2014).

Verga, L. & Kotz, S. A. Spatial attention underpins social word learning in the right fronto-parietal network. NeuroImage 195 , 165–173 (2019).

Thompson-Schill, S. Neuroimaging studies of semantic memory: inferring “how” from “where”. Neuropsychologia 41 (3), 280–292 (2003).

Bressler, S. & Menon, V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14 (6), 277–290 (2010).

Bassett, D. & Sporns, O. Network neuroscience. Nat. Neurosci. 20 (3), 353–364 (2017).

Li, P. & Grant, A. Second language learning success revealed by brain networks. Biling. Lang. Cogn. 19 , 657–664 (2016).

Hagoort, P. On Broca, brain, and binding: a new framework. Trends Cogn. Sci. 9 (9), 416–423 (2005).

Abutalebi, J. & Green, D. Bilingual language production: the neurocognition of language representation and control. J. Neurolinguist. 20 (3), 242–275 (2007).

Nichols, E. S. & Joanisse, M. F. Functional activity and white matter microstructure reveal the independent effects of age of acquisition and proficiency on second-language learning. NeuroImage 143 , 15–25 (2016).

Sun, X., Li, L., Ding, G., Wang, R. & Li, P. Effects of language proficiency on cognitive control: Evidence from resting-state functional connectivity. Neuropsychologia 129 , 263–275 (2019).

Noordzij, M. L. et al. Brain mechanisms underlying human communication. Front. Hum. Neurosci. 3 , 14 (2009).

Redcay, E. et al. Live face-to-face interaction during fMRI: a new tool for social cognitive neuroscience. NeuroImage 50 (4), 1639–47 (2010).

Hagoort, P. & Indefrey, P. The neurobiology of language beyond single words. Annu. Rev. Neurosci. 37 (1), 347–362 (2014).

Frith, C. & Frith, U. Social cognition in humans. Curr. Biol. 17 (16), R724–R732 (2007).

Adolphs, R. The social brain: neural basis of social knowledge. Annu. Rev. Psychol. 60 , 693–716 (2009).

Ferstl, E. C., Neumann, J., Bogler, C. & von Cramon, D. Y. The extended language network: a meta-analysis of neuroimaging studies on text comprehension. Hum. Brain. Mapp. 29 (5), 581–93 (2008).

Mason, R. & Just, M. The role of the theory-of-mind cortical network in the comprehension of narratives. Lang. Linguist. Compass 3 , 157–174 (2009).

Li, P. & Clariana, R. B. Reading comprehension in L1 and L2: An integrative approach. J. Neurolinguist. 50 , 94–105 (2019).

Peñaloza, C., Grasemann, U., Dekhtyar, M., Miikkulainen, R. & Kiran, S. BiLex: a computational approach to the effects of age of acquisition and language exposure on bilingual lexical access. Brain Lang ., in press, https://doi.org/10.1016/j.bandl.2019.104643 (2020).

Zhao, X. & Li, P. Simulating cross-language priming with a dynamic computational model of the lexicon. Biling.: Lang. Cogn. 16 , 288–303 (2013).

Li, P. Lexical organization and competition in first and second languages: computational and neural mechanisms. Cogn. Sci. 33 , 629–664 (2009).

Zhang, X., Yang, J., Wang, R. & Li, P. A neuroimaging study of semantic representation in first and second languages. Lang. Cogn. Neurosci . In press, https://doi.org/10.1080/23273798.2020.1738509 (2020).

Caldwell-Harris, C. L. Emotionality differences between a native and foreign language: implications for everyday life. Curr. Dir. Psychol. Sci. 24 , 214–219 (2015).

Pavlenko, A. Affective processing in bilingual speakers: disembodied cognition? Int. J. Psychol. 47 , 405–428 (2012).

Ivaz, L., Costa, A. & Dunabeitia, J. The emotional impact of being myself: emotions and foreign-language processing. J. Exp. Psychol. Learn. Mem. Cogn. 42 , 489–496 (2016).

Lambon Ralph, M. A., Jefferies, E., Patterson, K. & Rogers, T. The neural and computational bases of semantic cognition. Nat. Rev. Neurosci. 18 (1), 42–55 (2017).

Pulvermüller, F. How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics. Trends Cogn. Sci. 17 (9), 458–470 (2013).

Myers, L., LeWitt, R., Gallo, R. & Maselli, N. Baby FaceTime: Can toddlers learn from online video chat? Dev. Sci. 20 , e12430 (2017).

Lytle, S., Garcia-Sierra, A. & Kuhl, P. Two are better than one: Infant language learning from video improves in the presence of peers. PNAS 115 (40), 9859–9866 (2018).

Jeong, H., Li, P., Suzuki, W., Sugiura, M. & Kawashima, R. Neural mechanisms of language learning from social contexts, Manuscript under review (2020).

Dede, C. Immersive interfaces for engagement and learning. Science 323 (5910), 66–69 (2009).

Peeters, D. Virtual reality: a game-changing method for the language sciences. Psychon. Bull. Rev. 26 (3), 894–900 (2019).

Li, P., Legault, J., Klippel, A. & Zhao, J. Virtual reality for student learning: understanding individual differences. Hum. Behav. Brain 1 , 28–36 (2020).

Lan, Y. J., Fang, S. Y., Legault, J. & Li, P. Second language acquisition of Mandarin Chinese vocabulary: context of learning effects. Educ. Technol. Res. Dev. 63 (5), 671–690 (2015).

Hsiao, I. Y. T., Lan, Y. J., Kao, C. L. & Li, P. Visualization analytics for second language vocabulary learning in virtual worlds. J. Educ. Techno. Soc. 20 (2), 161–175 (2017).

Freund, J. et al. Emergence of individuality in genetically identical mice. Science 340 , 756–759 (2013).

Krokos, E., Catherine, P. & Amitabh, V. Virtual memory palaces: immersion aids recall. Virtual Real. 23 , 1–15 (2019).

Johnson-Glenberg, M. C., Birchfield, D. A., Tolentino, L. & Koziupa, T. Collaborative embodied learning in mixed reality motion-capture environments: two science studies. J. Educ. Psychol. 106 (1), 86–104 (2014).

Nature Editorial. STEM education: to build a scientist. Nature 523 (7560), 371–373 (2015).

Zhang, Y. et al. Neural signatures of phonetic learning in adulthood: a magnetoencephalography study. Neuroimage 46 , 226–240 (2009).

Yusa, N., Kim, J., Koizumi, M., Sugiura, M. & Kawashima, R. Social interaction affects neural outcomes of sign language learning as a foreign language in adults. Front. Hum. Neurosci. 11 , 115, https://doi.org/10.3389/fnhum.2017.00115 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Ullman, M. T. The neuronal basis of lexicon and grammar in first and second language: The declarative/procedural model. Biling. Lang. Cogn. 4 (1), 105–122 (2001).

Ullman, M. T. & Lovelett, J. T. Implications of the declarative/procedural model for improving second language learning: The role of memory enhancement techniques. Second Lang. Res. 34 (1), 39–65 (2018).

Goldin-Meadow, S. & Alibali, M. W. Gesture’s role in speaking, learning, and creating language. Annu. Rev. Psychol. 64 , 257–283 (2013).

Ozyurek, A. In Oxford Handbook of Psycholinguistics (eds. Rueschemeyer, S.A. & Gaskell, M.G.) 592–607 (Oxford University Press, 2018).

Ripollés, P. et al. The role of reward in word learning and its implications for language acquisition. Curr. Biol. CB 24 (21), 2606–11 (2014).

Dörnyei, Z. In Individual Differences and Instructed Language Learning (ed. Robinson, P.) 137–158 (John Benjamins, 2002).

MacIntyre, P. D. & Legatto, J. J. A dynamic system approach to willingness to communicate: Developing an idiodynamic method to capture rapidly changing affect. Appl. Linguist. 32 (2), 149–171 (2011).

Download references

Acknowledgements

The authors wish to thank the generous support to the research reported in this article by the US National Science Foundation’s Integrative Neural and Cognitive Systems (NCS) program (NCS-1533625; BCS-1633817) and by a Faculty Startup Fund from the Hong Kong Polytechnic University to PL and the MEXT KAKENHI Grant of Japan (#18K00776) to HJ. We thank Peter Hagoort for his helpful comments on an earlier version of this article.

Author information

Authors and affiliations.

Department of Chinese and Bilingual Studies, Faculty of Humanities, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

Graduate School of International Cultural Studies & Department of Human Brain Science, Institute of Development, Aging, and Cancer, Tohoku University, Aoba-ku, Sendai, Japan

Hyeonjeong Jeong

You can also search for this author in PubMed   Google Scholar

Contributions

P.L. and H.J. conceptualized the article. P.L. provided the overall framework of the article, and P.L. and H.J. both reviewed the literature and wrote the article.

Corresponding author

Correspondence to Ping Li .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Li, P., Jeong, H. The social brain of language: grounding second language learning in social interaction. npj Sci. Learn. 5 , 8 (2020). https://doi.org/10.1038/s41539-020-0068-7

Download citation

Received : 17 December 2019

Accepted : 29 May 2020

Published : 19 June 2020

DOI : https://doi.org/10.1038/s41539-020-0068-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Brain gray matter morphometry relates to onset age of bilingualism and theory of mind in young and older adults.

• Xiaoqian Li
• Kwun Kei Ng
• W. Quin Yow

Scientific Reports (2024)

Knee flexion of saxophone players anticipates tonal context of music

• Nádia Moura

npj Science of Learning (2023)

Psychological Impact of Languages on the Human Mind: Research on the Contribution of Psycholinguistics Approach to Teaching and Learning English

Journal of Psycholinguistic Research (2023)

RETRACTED ARTICLE: IoT Based Multimodal Social Interaction Activity Framework for the Physical Education System

• Liang Zhuang
• Ching-Hsien Hsu
• Priyan Malarvizhi Kumar

Wireless Personal Communications (2022)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

EDITORIAL article

Editorial: second or foreign language learning and cognitive development.

• 1 Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
• 2 School of Foreign Languages, Huazhong University of Science and Technology, Wuhan, Hubei, China
• 3 School of Foreign Languages, Nanjing Normal University, Nanjing, Jiangsu, China

Editorial on the Research Topic Second or foreign language learning and cognitive development

Research on bilingualism has shown that acquiring a second language enhances a learner's executive function and metalinguistic awareness within the cognitive development domain ( Bialystok, 2001 ; Bialystok and Luk, 2012 ; Kroll and Bialystok, 2013 ). Further investigation is necessary to understand the impact of individual differences on the learning process and its result, including the influencing factors like age, gender, gender, first language, learning style, input and feedback types, and teaching methods. Perhaps more crucially, however, is the potential for the learning process to interact with learners' metalinguistic, affective, cognitive, and metacognitive abilities ( Dörnyei, 2009 ; Bylund and Jarvis, 2011 ; Gass and Mackey, 2015 ; VanPatten and Williams, 2015 ).

A total of 29 manuscripts were submitted on the Research Topic. A total of 15 have been accepted, which fall into five broad categories: (1) the interaction between L1 and L2; (2) second or foreign language learning and cognitive controls; (3) second or foreign language learning and social skills and empathy; (4) second or foreign language learning and metacognitive skills; and (5) teaching methodology and L2 and foreign language learning.

One paper in particular, “ Australian English listeners' perception of Japanese vowel length reveals underlying phonological knowledge ,” by Yazawa et al. , examines how native speakers of Australian English, who typically emphasize vowel length compared with most other English varieties, perceive Japanese vowel length contrasts. In a forced-choice study, twenty monolingual Australian English speakers were asked to rank the Japanese long and short vowels based on their resemblance to their native vowel categories. The findings indicated a general tendency for Australian English long and short vowels (such as/i:, I/as in “heed,” “hid”) to be classified as Japanese long and short vowels (e.g.,/ii, i/). This contrasts with the literature-reported categorization of all Japanese vowels as tense by American English listeners, regardless of length (e.g.,/ii, i/as both “heed”). The result is consistent with a feature-based speech perception approach.

Research on the shared-dialect effect, which suggests that raters who share a candidate's dialect may provide higher scores on English speaking examinations, was conducted by Xu et al. . Oral performance in the recounting task of the computer-based English Listening and Speaking Test was evaluated by raters proficient in Cantonese and Mandarin. No statistically significant interaction was found between the raters' and candidates' dialects, nor were there any significant variations in the ratings given by either group in the quantitative data. The understanding and scoring process of raters were influenced by their awareness and familiarity with accents, according to the qualitative data.

Wang D. et al. investigate the efficacy and diversity of translation strategies employed by Chinese English as a foreign language (EFL) learners when addressing light verb constructions (LVCs), a significant distinction between Chinese and English, in a different study titled “Walking out of the light verb jungle: Exploring the translation strategies of light verb constructions in Chinese–English consecutive interpreting”. The study examines the methods used by 66 Chinese EFL learners to interpret 12 target LVCs using a theory-driven, context-based interpreting problem. The outcomes demonstrate the typical structural trends in LVC translation as well as the overall preferences for strategy selection among Chinese EFL learners. Additionally, the study reveals a positive relationship between vocabulary knowledge and the acceptability rates of LVCs, indicating the necessity of integrating constructional teaching into EFL instruction.

The research paper titled “ Non-adjacent dependency learning from variable input: investigating the effects of bilingualism, phonological memory, and cognitive control ” by Verhagen and de Bree delves into L2 learning and cognitive control. It sheds new light on the correlation between bilingualism and statistical learning, and compares the effects of consistent and variable input on statistical learning in both monolingual and bilingual children and adults. The study also investigates whether phonological memory and cognitive control play a role in potential group differences. The results indicate that bilinguals have a limited advantage in statistical learning, which is not consistently linked to enhanced cognitive abilities associated with bilingualism.

In recent years, there has been a surge in research on the relationship between emotion and L2 learning, with a particular focus on social skills and empathy. One such study, “ Understanding foreign language writing anxiety and its correlates ” by Li , conducted a quantitative meta-analysis of 84 effect sizes from 22 primary studies to investigate the connections between foreign language writing anxiety and its high and low-evidence correlates. The study revealed moderate correlations between foreign language writing anxiety and writing self-efficacy and performance, as well as moderately positive effects with listening, speaking, and reading anxiety. Additionally, the study found that age and language proficiency have significant moderating effects. The findings have important pedagogical implications, which were discussed based on the results.

Wang H. et al. 's article titled “ Unpacking the relationships between emotions and achievement of EFL learners in China: Engagement as a mediator ” explores the connections between learners' emotions, such as foreign language enjoyment (FLE), foreign language classroom anxiety (FLCA), and foreign language learning boredom (FLLB), and engagement, as well as their English achievement. The study involved 907 English as a foreign language (EFL) learners from a university in China who completed an online questionnaire, and structural equation modeling was used to test the hypothesized relations among the variables. The results showed correlations between learners' FLE, FLCA, and FLLB, and that learners' engagement mediated the relationships between their emotions and English achievement. The study provides evidence for the mechanism underlying the relationships between emotions, engagement, and achievement, and sheds light on EFL teaching and learning at the tertiary level in China.

A third article that falls into this category, Measuring Chinese English-as-a-foreign-language learners' resilience: development and validation of the foreign language learning resilience scale by Guo and Li , aimed to develop the Foreign Language Learning Resilience Scale (FLLRS) to measure the psychometric scale reliability and validity of foreign language learning resilience in Chinese English-as-a-foreign-language contexts. Data was collected from 313 Chinese college students, and the FLLRS was validated based on reliability and validity tests. The FLLRS consisted of three factors: ego resilience, metacognitive resilience, and social resilience, all contributing to foreign language learning resilience. Metacognitive resilience had the highest path coefficient, followed by social resilience and ego resilience. The validated scale could advance knowledge in second language acquisition regarding the factors that affect foreign language learning resilience.

Second or foreign language learning and metacognitive skills have been of great interest among researchers. In a study by Qin et al. , entitled “ Validation of metacognitive strategies in writing and their predictive effects on the writing performance of English as foreign language student writers ,” the metacognitive writing strategies of EFL college students in China were examined through a survey and a writing test. The study utilized exploratory factor analysis and confirmatory factor analysis to analyze the data, and multiple regression analysis was employed to understand the predictive effects of metacognitive strategies on writing performance. The findings suggest that writing instruction can enhance students' awareness and ability to acquire metacognitive writing strategies, particularly those related to planning, monitoring, and evaluating.

Wang's study, “ Text memorization: an effective strategy to improve Chinese EFL learners' argumentative writing proficiency ,” explored the impact of text memorization strategies on the argumentative writing proficiency of EFL learners in China. The study focused on the text memorization process and the strategies used by learners to enhance memorization. Thirty-three Chinese English majors participated in seven text memorization tests, a pre-test, and a post-test to evaluate their memorization outcomes and writing proficiency before and after memorizing seven model English writings. Additionally, twelve top scorers in the memorization tests were interviewed. The results indicated that text memorization significantly improved learners' writing proficiency. Moreover, a new system of text memorization strategies was developed to assist scholars and teachers in enhancing the writing skills of EFL learners.

Peng and Bao's article, “ Effects of reasoning demands triggered by genre on Chinese EFL learners' writing performance ,” examined the impact of cognitive complexity on the writing performance of advanced Chinese EFL learners in two different genres: expository writing and argumentative writing. The study involved 76 EFL learners who completed two writing tasks with varying levels of reasoning demands. Multiple measure indices, including lexical complexity, syntactic complexity, accuracy, fluency, and cohesion, were used to assess the differences in production dimensions between the two tasks. The results indicated that cognitive complexity significantly enhanced lexical complexity, clausal complexity, and cohesion, but there was a trade-off effect for phrasal and clausal structures within syntactic complexity. The findings of this study have important implications for the sequencing and design of L2 writing tasks.

The success of foreign students' academic and life skills in the Northern Cyprus region is heavily reliant on the importance given to Turkish language teaching. “ Teaching the Turkish language to foreigners at higher education level in northern Cyprus: an evaluation based on self-perceived dominant intelligence types, twenty-first century skills and learning technologies ” by Kurt and Güneyli aimed to investigate how college students use learning technology, 21st-century skills, and perceive intelligence categories in learning a foreign language. The study utilized purposeful and convenience sampling, selecting the institution with the largest number of international students in Northern Cyprus. The results indicated a statistically significant correlation between 21st-century skills and foreign language-learning technology usage, highlighting the importance of modern methodologies and social learning in foreign language education.

Zhao and Huang's article, “ A comparative study of frequency effect on acquisition of grammar and meaning of words between Chinese and foreign learners of English language ,” investigated the impact of frequency on L2 vocabulary acquisition. The study explored the frequency effect on the acquisition of grammar and meaning of alphabetic words between Chinese learners of hieroglyphic language and foreign learners of alphabetic language. The results indicated that mother tongue type may not be the factor causing differences in grammar and meaning acquisition of vocabulary, while exposure frequency of vocabulary plays a determining role. Furthermore, learner types, language types, frequency, and part of speech of a word have an interaction effect on word acquisition. The study sheds light on the importance of frequency in L2 vocabulary acquisition and highlights the need for tailored teaching methods to facilitate this process.

Meng et al.'s article, “ Cognitive diagnostic assessment of EFL learners' listening barriers through incorrect responses ,” utilized Cognitive Diagnostic Models (CDMs) for bugs, or Bug-CDMs, to diagnose EFL learners' listening barriers through incorrect responses. The study found that Bug-GDINA was the optimal model, and semantic understanding and vocabulary recognition were the most prevalent barriers. The findings demonstrate the feasibility of using Bug-GDINA to diagnose listening barriers from incorrect responses.

The majority of research on collocations in L2 acquisition and cognitive psychology has focused on phonographic languages, giving scant attention to ideographic languages such as Chinese and Japanese. In “ The lexical processing of Japanese collocations by Chinese Japanese-as-a-Foreign-Language learners: an experimental study by manipulating the presentation modality, semantic transparency, and translational congruency ,” Song et al. investigated the processing of Japanese collocations by 36 Chinese Japanese-as-a-Foreign-Language learners. The study manipulated presentation modality, semantic transparency, and translational congruency in a lexical judgment task. The results indicated longer reaction times for auditory presentation than visual presentation, and longer reaction times for high semantic transparency and congruent translation in auditory presentation. These findings support the dual-route model of Japanese collocational processing and demonstrate that presentation modality, semantic transparency, and translational congruency have an impact on processing.

He and Gao's study, “ Explicating peer feedback quality and its impact on feedback implementation in EFL writing ,” investigated the impact of peer feedback quality on EFL students' feedback implementation in argumentative writing tasks. The researchers developed a measuring scale with two dimensions to assess feedback quality, including accuracy and revision potential. The results indicated that feedback accuracy was at a medium level, while revision potential was at a low level, with accuracy having a stronger predictive power on implementation. Furthermore, feedback quality had the strongest predictive power when feedback features and focus were considered. The study highlights the importance of training students to provide and implement high-quality feedback marked by good accuracy and high revision potential in future instructions.

For future studies on second or foreign language learning and learners' cognitive development, it would be valuable to explore how the learning process, and learning multiple foreign languages, either simultaneously or consecutively, can impact learners' emotional and cognitive development, and how the new development, in turn, affects the learning process and outcome. This research can provide insights into the relationship between language learning and cognitive development, as well as the potential benefits of multilingualism. Additionally, it may be interesting to investigate how individual differences, such as age, gender, and learning styles, affect this relationship. Understanding the impact of language learning on cognitive development can inform language education and help educators design more effective language learning programs.

Author contributions

DS: Writing—review & editing. JX: Writing—review & editing. HZ: Writing—review & editing. ZT: Writing—review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Bialystok, E. (2001). Bilingualism in Development: Language, Literacy, and Cognition . Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511605963

Crossref Full Text | Google Scholar

Bialystok, E., and Luk, G. (2012). The cognitive benefits of bilingualism. Res. Lang. Soc. Inter. 45, 108–118.

Google Scholar

Bylund, E., and Jarvis, S. (2011). L2 effects on L1 event conceptualization. Lang. Lear. 61, 446–486. doi: 10.1017/S1366728910000180

Dörnyei, Z. (2009). The Psychology of Second Language Acquisition . Oxford: Oxford University Press.

Gass, S. M., and Mackey, A. (2015). “Input, interaction, and output in second language acquisition,” in The Routledge Handbook of Second Language Acquisition (London: Routledge), 64–79.

Kroll, J. F., and Bialystok, E. (2013). Understanding the consequences of bilingualism for language processing and cognition. J. Cogn. Psychol. 25, 497–514. doi: 10.1080/20445911.2013.799170

PubMed Abstract | Crossref Full Text | Google Scholar

VanPatten, B., and Williams, J. (2015). Theories in Second Language Acquisition: An Introduction. London: Routledge. doi: 10.4324/9780203628942

Keywords: second language (L2) acquisition, foreign language (English) learning and teaching, cognitive development, language teaching, language learning

Citation: Shu D, Xu J, Zhang H and Tian Z (2024) Editorial: Second or foreign language learning and cognitive development. Front. Psychol. 14:1354329. doi: 10.3389/fpsyg.2023.1354329

Received: 12 December 2023; Accepted: 20 December 2023; Published: 04 January 2024.

Edited and reviewed by: Xiaolin Zhou , Peking University, China

Copyright © 2024 Shu, Xu, Zhang and Tian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Dingfang Shu, shudfk@yahoo.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Hum Neurosci

Learning a Foreign Language: A Review on Recent Findings About Its Effect on the Enhancement of Cognitive Functions Among Healthy Older Individuals

Currently, there is an increasing number of older population groups, especially in developed countries. This demographic trend, however, may cause serious problems, such as an increase in aging diseases, one of which is dementia whose main symptom consists in the decline of cognitive functioning. Although there has been ongoing pharmacological research on this neurological disorder, it has not brought satisfying results as far as its treatment is concerned. Therefore, governments all over the world are trying to develop alternative, non-pharmacological strategies/activities, which could help to prevent this cognitive decline while this aging population is still healthy in order to reduce future economic and social burden. One of the non-pharmacological approaches, which may enhance cognitive abilities and protect against the decline in healthy older population, seems to be the learning of a foreign language. The purpose of this mini-review article is to discuss recent findings about the effect of foreign language learning on the enhancement of cognitive functions among healthy older individuals. The findings, divided into three research areas, show that the learning of a foreign language may generate a lot of benefits for older individuals, such as enhancement of cognitive functioning, their self-esteem, increased opportunities of socializing, or reduction of costs. However, as Ware et al. ( 2017 ) indicate, any intervention program on foreign language learning should be well thought of and tailored to the needs of older people in order to be effective and avoid accompanying factors, such as older people’s anxiety or low self-confidence. Nevertheless, more empirical studies should be done in this area.

Introduction

The population is aging. For example, in Europe, older people aged 65+ years form 18% of the whole population. It is expected that by 2050, the older population will outnumber the young population in many developed countries (Statista, 2017 ). This demographic trend, however, may cause serious problems, such as an increase in aging diseases, one of which is dementia whose main symptom consists in the decline of cognitive functioning. This is connected with the brain atrophy, particularly in the temporal cortex, the region that is related to declarative memory (see Buckner, 2004 ), which is encoded by the hippocampus, entorhinal cortex and perirhinal cortex, loss of synaptic connections (Maston, 2010 ), and the occurrence of neuropathological symptoms associated with dementia (see Antoniou and Wright, 2017 ). Although there has been ongoing pharmacological research on this neurological disorder, it has not brought satisfying results as far as its treatment is concerned (Karakaya et al., 2013 ).

Therefore, governments all over the world are trying to develop alternative, non-pharmacological strategies/activities, which could help to prevent this cognitive decline while this aging population is still healthy in order to reduce future economic and social burden (Maresova et al., 2016 ). These alternative, non-pharmacological intervention therapies can be divided into several groups, which have a positive impact on the enhancement of cognitive functions: physical activities, cognitive training, healthy diet (see Klimova and Kuca, 2015 ), as well as social enhancement interventions (see Ballesteros et al., 2015 ), including the use of modern information and communication technologies (Peter et al., 2013 ; Ballesteros et al., 2014 ). One of the cognitive training activities, which may enhance cognitive abilities and protect against the decline in healthy older population, seems to be the learning of a foreign language (see Antoniou et al., 2013 ; Kroll and Dussias, 2017 ). As Connor ( 2016 ) points out, learning a foreign language can promote thinking skills, increase mental agility and delay the aging of the brain. However, as Kurdziel et al. ( 2017 ) explain, the retrieval of new words among older people is harder since their fluid intelligence (i.e., the ability to reason and solve things), as well as the working, short-term, memory (i.e., management of immediately available information) are getting affected in the course of aging. On the contrary, their crystallized intelligence (i.e., the ability to use experience, knowledge and skills) remain intact in the aging process (see Kavé et al., 2008 ). Kurdziel et al. ( 2017 ) also state that the decline in language ability among older people is slower than the decline in global memory. In addition, older individuals possess a superior raw vocabulary even if compared with well-educated adults of young generation. In addition, foreign language learning does not have any side effect (Bak, 2016 ) and can help reduce country’s economic burden (Bialystok et al., 2016 ). Simply, it does not do any harm (see Strauss, 2015 ). Abutalebi and Clahsen ( 2015 ) present that knowledge about language processing in older individuals and about the potential factors that prevent cognitive decline is currently very much desirable since it may contribute to preparing for the demographic changes which our society faces.

The purpose of this mini-review article is to discuss recent findings about the effect of foreign language learning on the enhancement of cognitive functions among healthy older individuals.

The methodology of this mini-review article is based on Moher et al. ( 2009 ). Studies were selected on the basis of the following keyword collocations: healthy aging and foreign language learning ; healthy older individuals and foreign language learning , healthy older individuals and bilingualism , found in the world’s acknowledged databases: Web of Science, PubMed, Scopus and ScienceDirect. The search was not limited by time since the studies on the research topic were scarce. Altogether 43 studies, including both review and original articles, were detected, most of them were identified in ScienceDirect and Web of Science, followed by PubMed and Scopus. The analysis was done by identifying the key words and checking duplication of available sources in the databases mentioned above. Afterwards, the studies were assessed for their relevancy, i.e., verification on the basis of abstracts whether the selected study corresponds to the set goal. After the exclusion of such studies, 26 studies remained for the full-text analysis. Out of 26 studies, 12 were empirical or randomized control studies, which are in detail described in Table ​ Table1. 1 . The review studies (e.g., Antoniou et al., 2013 ; Lee and Tzeng, 2016 ; Kurdziel et al., 2017 ), the studies dealing with the younger adults (e.g., Schlegel et al., 2012 ; Bellander et al., 2016 ) and the studies with patients suffering from dementia, respectively Alzheimer’s disease (e.g., Woumans et al., 2015 ; Bialystok et al., 2016 ) were used for comparison reasons. Moreover, the author also explored websites connected with the research topic, e.g., SeniorsMatter ( 2017 ).

An overview of the detected empirical studies on the effect of foreign language learning on the enhancement of cognitive functions among healthy older individuals.

Findings and Their Discussion

As it has been stated in the “Methods” section, there is a lack of studies on the learning of a foreign language and its effect on the enhancement of cognitive functioning in older people, apart from those on bilingualism (see Klimova et al., 2017a ). Overall, the identified studies can be divided into three main areas: studies concerning the brain plasticity in the old age and foreign language learning; studies focused on foreign language learning among healthy older individuals; and studies aimed at bilingualism and healthy aging, including the electrophysiological studies. All of them also discuss the cognitive aspects.

Plasticity of the Brain in the Old Age and Foreign Language Learning

The brain remains with considerable plasticity even in the old age. Although there is some neural deterioration that rises with age, the brain has the capacity to increase neural activity and develop neural scaffolding to regulate cognitive function (Park and Reuter-Lorenz, 2009 ; Reuter-Lorenz and Park, 2014 ). For example, Cheng et al. ( 2015 ) maintain that both short-term and long-term period of foreign language learning may lead to the changes in the structure of the brain, which consequently may contribute to the promotion of the cognitive reserve, i.e., the resilience to neuropathological damage of the brain (Stern, 2013 ). This has been also confirmed by Lee and Tzeng ( 2016 ), who claim that foreign language learning results in effective structural as well as functional connectivity in the brain due to neural plasticity. They indicate that the effective connectivity due to foreign language learning enhances the capacity for language processing and general executive control by reorganizing neural circuitries. Furthermore, research shows that foreign language learning has a positive impact on both white and gray matter structures (see Bellander et al., 2016 ). For instance, Schlegel et al. ( 2012 ) in their randomized controlled study with 11 English speakers (average age of 20 years) who took a 9-month intensive course in written and spoken Modern Standard Chinese and 16 controls who did not study a language reported that the plasticity of the white matter played a significant role in adult language learning. Although their adult learners showed progressive changes in white matter tracts, associated with traditional left hemisphere language areas and their right hemisphere analogs, the most important changes appeared in frontal lobe tracts crossing the genu of the corpus callosum-a region, which is not generally involved in current neural models of language processing. Tyler et al. ( 2010 ) in their study on preserved syntactic processing across the life span, argue that this is caused by the shift from a primarily left hemisphere frontotemporal system to a bilateral functional language network. In addition, Connor ( 2016 ) described a study of retired people doing an intensive language course of 5 h a day on the Isle of Skye to learn Gaelic (see Bak et al., 2016 ). After finishing the course, the scientists discovered these people were more mentally agile than those doing a course on something else. As Antoniou et al. ( 2013 ) indicate, foreign language training may engage a larger brain network than other forms of cognitive training that have been investigated (e.g., math and crossword puzzles), and it is likely to require long distance neural connections. However, not all the findings on the plasticity o the brain and aging process are positive. For instance, the controlled study by Ramos et al. ( 2017 ) maintains that the switching ability (i.e., the ability to shift attention between one task and another) was not enhanced by learning a foreign language, in this case Basque language, among elderly Spanish people.

Foreign Language Learning Among Healthy Older Individuals

In the most recent study on foreign language learning and its effect on cognitive functioning, Ware et al. ( 2017 ) developed a technology-based English training program for older French adults. The program was based on the assumptions provided by Antoniou et al. ( 2013 ). These assumptions involved various factors, such as that computer-based language training can be administered anywhere and at any time to suit learner’s needs, the content can be adjusted and items can be repeated. In addition, learners can socialize. The average age of the participants was 75 years. The course lasted for 4 months and consisted of 16 2-h sessions. The researchers used standardized tests for measuring cognitive functions (Montreal Cognitive Assessment), as well as University of California Loneliness Assessment for measuring subjective feelings of loneliness and social isolation, both of which did not significantly change after finishing the course. Nevertheless, the researchers found out that their program was feasible for this age group and the participants enjoyed it. Similarly, research performed by Bak et al. ( 2016 ) on a short 1-week Scottish Gaelic course on attentional functions among 67 adults aged between 18 years and 78 years reveals that even a short period of intensive language learning can modulate attentional functions and that all age groups can benefit from this effect. The results showed that at the beginning there was no difference between the groups. However, at the end of the course, a considerable improvement in attention switching was detected in the language group ( p < 0.001) but not the control group ( p = 0.127), independent of the age of subjects. In addition, they also suggested that these short-term effects could be maintained through continuous practice, but the minimum study period should be 5 h a week.

Research also indicates that the age in second language acquisition is not such a significant factor, but the length of exposure to the target language is important (Bialystok, 1997 ). In fact, on the one hand, it might take older people longer and more practice to learn a foreign language in the old age because of difficulty distinguishing new sounds and retrieve novel words, but on the other hand, they are more relaxed and motivated to learn (see SeniorsMatter, 2017 ). As it has been already pointed out, the main problem for older people is to retrieve new words (see Kurdziel et al., 2017 ). However, they are able to retain these new words easily if they are provided in the context. Kurdziel et al. ( 2017 ) also revealed that newly learned words were stored in hippocampus during encoding and then integrated into lexicon in the course of sleeping. Nevertheless, the quality of sleeping is often negatively affected in the old age and therefore older people are not able to retain as many words as their younger counterparts whose sleeping period is higher and unbroken.

Diaz-Orueta et al. ( 2012 ) report that the main stimulation for older people to learn a foreign language is a challenge, socialization, fun, providing learning opportunities and escape from daily routine. Moreover, the older individuals might also have experience of learning a foreign language, which can help them in acquiring a new language (see Singleton and Lengyel, 1995 ).

Kurdziel et al. ( 2017 ) expand by suggesting that learning throughout aging should be a must because older people who keep mentally and physically active are less likely to be cognitively impaired and depressed. In fact, depression seems to be one of the most serious comorbidities in the aging process (Popa-Wagner et al., 2014 ; Sandu et al., 2015 ). Furthermore, foreign language learning increases self-confidence, enables older people travel and communicate with their peers in foreign countries.

Bilingualism and Healthy Aging

The theory of bilingualism states that people acquiring a second language in their adulthood may prevent cognitive decline in later life by approximately 4.5 years (see Bialystok et al., 2007 , 2016 ; Bak et al., 2014 ; Wilson et al., 2015 ; Woumans et al., 2015 ). In their recent study, on the basis of measures of cognitive function and brain structure, Bialystok et al. ( 2016 ) show that bilingualism can delay cognitive decline. As Bialystok et al. ( 2004 ) and Bialystok ( 2006 ) state, bilingualism contributes to compensate age-related losses in certain executive processes. Furthermore, bilingual people possess better mental flexibility because they are used to adapting to constant changes and processing information in a more effective way than the monolingual individuals. However, these results especially concern the retrospective studies on bilingualism since the prospective studies on bilingualism, such as Lawton et al. ( 2015 ), Sanders et al. ( 2012 ), Yeung et al. ( 2014 ), or Zahodne et al. ( 2014 ), have not exerted significant results in this respect (see Klimova et al., 2017a ). For instance, Mukadam et al. ( 2017 ) in the most recent study revealed that retrospective studies inclined to confounding by education, or cultural differences in presentation to dementia and are thus not relevant to set causative links between risk factors and results. However, the electrophysiological studies on bilingualism indicate that bilingualism may enhance cognitive functions among healthy older individuals (i.e., Kousaie and Phillips, 2017 ). Moreover, as Ansaldo et al. ( 2015 ) state, healthy older bilinguals deal with interference control without recruiting a circuit that is particularly vulnerable to aging.

Table ​ Table1 1 below then summarizes the main findings of the studies on the effect of foreign language learning on the enhancement of cognitive functions for healthy older individuals.

The limitations of this mini-review study mainly involve a lack of relevant studies on the research topic. This fact may cause the overestimated effects of the results, which may have an adverse impact on the validity of these reviewed studies (see Melby-Lervåg and Hulme, 2016 ).

Overall, some of the findings in Table ​ Table1, 1 , as well as from other mentioned studies indicate that the learning of a foreign language may generate benefits for older individuals, such as enhancement of cognitive functioning (Bak et al., 2014 , 2016 ; Ansaldo et al., 2015 ; Kousaie and Phillips, 2017 ) their self-esteem (Ware et al., 2017 ), or increased opportunities of socializing (Diaz-Orueta et al., 2012 ; Ballesteros et al., 2015 ). Bialystok et al. ( 2016 ) also emphasize that second-language learning has long-term implications for public health in terms of cost-effectiveness. In addition, as Ware et al. ( 2017 ) indicate, any intervention program on foreign language learning should be well thought of and tailored to the needs of older people in order to be effective and avoid accompanying factors, such as older people’s anxiety or low self-confidence.

In comparison with the intervention studies focusing on physical activities (see Klimova et al., 2017b ), there is still smaller evidence of the effect of foreign language learning on the enhancement of cognitive functions among the healthy aging population. This is especially caused by a lack of research in this area.

Author Contributions

BK drafted, analyzed, wrote and read the whole manuscript herself.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. This article is supported by the project Excellence (2018) at the Faculty of Informatics and Management of the University of Hradec Králové, Czechia.

• Abutalebi J., Clahsen H. (2015). Bilingualism, cognition, and aging. Editorial . Biling. Lang. Cogn. 18 , 1–2. 10.1017/s1366728914000741 [ CrossRef ] [ Google Scholar ]
• Ansaldo A. I., Ghazi-Saidi L., Adrover-Roig D. (2015). Interference control in elderly bilinguals: appearances can be misleading . J. Clin. Exp. Neuropsychol. 37 , 455–470. 10.1080/13803395.2014.990359 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Antoniou M., Gunasekera G., Wong P. C. M. (2013). Foreign language training as cognitive therapy for age-related cognitive decline: a hypothesis for future research . Neurosci. Biobehav. Rev. 37 , 2689–2698. 10.1016/j.neubiorev.2013.09.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Antoniou M., Wright S. M. (2017). Uncovering the mechanisms responsible for why language learning may promote healthy cognitive aging . Front. Psychol. 8 :2217. 10.3389/fpsyg.2017.02217 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bak T. H. (2016). Language lessons to help protect against dementia . BMJ 354 :i5039. 10.1136/bmj.i5039 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bak T. H., Long M. R., Vega-Mendoza M., Sorace A. (2016). Novelty, challenge and practice: the impact of intensive language learning on attentional functions . PLoS One 11 :e0153485. 10.1371/journal.pone.0153485 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bak T. H., Nissan J. J., Allerhand M. M., Deary I. J. (2014). Does bilingualism influence cognitive aging? Ann. Neurol. 75 , 959–963. 10.1002/ana.24158 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ballesteros S., Kraft E., Santana S., Tziraki C. (2015). Maintaining older brain functionality: a targeted review . Neurosci. Biobehav. Rev. 55 , 453–477. 10.1016/j.neubiorev.2015.06.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ballesteros S., Toril P., Mayas J., Reales J. M., Waterworth J. A. (2014). An ICT social network in support of successful ageing . Gerontechnology 13 , 37–46. 10.4017/gt.2014.13.1.007.00 [ CrossRef ] [ Google Scholar ]
• Bellander M., Berggren R., Mårtensson J., Brehmer Y., Wenger E., Li T. Q., et al.. (2016). Behavioral correlates of changes in hippocampal gray matter structure during acquisition of foreign vocabulary . Neuroimage 131 , 205–213. 10.1016/j.neuroimage.2015.10.020 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bialystok E. (1997). The structure of age: in search of barriers to second language acquisition . Second Lang. Rese. 13 , 116–137. 10.1191/026765897677670241 [ CrossRef ] [ Google Scholar ]
• Bialystok E. (2006). Effect of bilingualism and computer video game experience on the Simon task . Can. J. Exp. Psychol. 60 , 68–79. 10.1037/cjep2006008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bialystok E., Abutalebi J., Bak T. H., Burke D. M., Kroll J. (2016). Aging in two languages: implications for public health . Ageing Res. Rev. 27 , 56–60. 10.1016/j.arr.2016.03.003 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bialystok E., Craik F. I. M., Freedman M. (2007). Bilingualism as a protection against the onset of symptoms of dementia . Neuropsychologia 45 , 459–464. 10.1016/j.neuropsychologia.2006.10.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bialystok E., Craik F. I. M., Klein R., Viswanathan M. (2004). Bilingualism, aging, and cognitive control: evidence from the Simon task . Psychol. Aging 19 , 290–303. 10.1037/0882-7974.19.2.290 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buckner R. L. (2004). Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate . Neuron 44 , 195–208. 10.1016/j.neuron.2004.09.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheng K. W., Deng Y. H., Li M., Yan H. M. (2015). The impact of L2 learning on cognitive aging . ADMET 3 , 260–273. 10.5599/admet.3.3.206 [ CrossRef ] [ Google Scholar ]
• Connor S. (2016). Learning second language can delay ageing of the brain, say scientists . Available online at: http://www.independent.co.uk/news/science/learning-second-language-can-delay-ageing-of-the-brain-say-scientists-a6873796.html [Accessed on December 28, 2017].
• Diaz-Orueta U., Facal D., Nap H. H., Ranga M. M. (2012). What is the key for older people to show interest in playing digital learning games? Initial qualitative findings from the LEAGE project on a multicultural European sample . Games Health J. 1 , 115–123. 10.1089/g4h.2011.0024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karakaya T., Fußer F., Schröder J., Pantel J. (2013). Pharmacological treatment of mild cognitive impairment as a prodromal syndrome of Alzheimer’s disease . Curr. Neuropharmacol. 11 , 102–108. 10.2174/157015913804999487 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kavé G., Eyal N., Shorek A., Cohen-Mansfield J. (2008). Multilingualism and cognitive state in the oldest old . Psychol. Aging 23 , 70–78. 10.1037/0882-7974.23.1.70 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klimova B., Kuca K. (2015). Alzheimer’s disease: potential preventive, non-invasive, intervention strategies in lowering the risk of cognitive decline—a review study . J. Appl. Biomed. 13 , 257–261. 10.1016/j.jab.2015.07.004 [ CrossRef ] [ Google Scholar ]
• Klimova B., Valis M., Kuca K. (2017a). Bilingualism as a strategy to delay the onset of Alzheimer’s disease . Clin. Interv. Aging 12 , 1731–1737. 10.2147/CIA.s145397 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klimova B., Valis M., Kuca K. (2017b). Cognitive decline in normal aging and its prevention: a review on non-pharmacological lifestyle strategies . Clin. Interv. Aging 12 , 903–910. 10.2147/CIA.s132963 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kousaie S., Phillips N. A. (2017). A behavioral and electrophysiological investigation of the effect of bilingualism on aging and cognitive control . Neuropsychologia 94 , 23–35. 10.1016/j.neuropsychologia.2016.11.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kroll J. F., Dussias P. E. (2017). The benefits of multilingualism to the personal and professional development of residents of the US . Foreign Lang. Ann. 50 , 248–259. 10.1111/flan.12271 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kurdziel L. B. F., Mantua J., Spencer R. M. C. (2017). Novel word learning in older adults: a role for sleep? Brain Lang. 167 , 106–113. 10.1016/j.bandl.2016.05.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lawton D. M., Gasquoine P. G., Weimer A. A. (2015). Age of dementia diagnosis in community dwelling bilingual and monolingual Hispanic Americans . Cortex 66 , 141–145. 10.1016/j.cortex.2014.11.017 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee R. R. W., Tzeng O. J. L. (2016). Neural bilingualism: a new look at an old problem . Lang. Linguist. 17 , 147–193. 10.1177/1606822X15614523 [ CrossRef ] [ Google Scholar ]
• Maresova P., Klimova B., Novotny M., Kuca K. (2016). Alzheimer’s disease and Parkinson’s diseases: expected economic impact on Europe—a call for a uniform European strategy . J. Alzheimers Dis. 54 , 1123–1133. 10.3233/JAD-160484 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Maston L. (2010). Declarative (explicit) and procedural (implicit) memory . Available online at: http://www.human-memory.net/types_declarative.html [Accessed on March 11, 2018].
• Melby-Lervåg M., Hulme C. (2016). There is no convincing evidence that working memory training is effective: a reply to Au et al. (2014) and Karbach and Verhaeghen (2014) . Psychon. Bull. Rev. 23 , 324–330. 10.3758/s13423-015-0862-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moher D., Liberati A., Tetzlaff J., Altman D. G. (2009). The PRISMA Group. Preferred reporting items for systematic review and meta-analysis: the PRISMA statement . PLoS Med. 6 :e1000097. 10.1371/journal.pmed.1000097 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mukadam N., Sommerland A., Livingston G. (2017). The relationship of bilingualism compared to monolingualism to the risk of cognitive decline or dementia: a systematic review and meta-analysis . J. Alzheimers Dis. 58 , 45–54. 10.3233/JAD-170131 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Park D. C., Reuter-Lorenz P. (2009). The adaptive brain: aging and neurocognitive scaffolding . Annu. Rev. Psychol. 60 , 173–196. 10.1146/annurev.psych.59.103006.093656 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Peter C., Kreisner A., Schröter M., Hyosun K., Bieber G., Öhberg F., et al. (2013). AGNES: connecting people in a multimodal way . J. Multimod. User Interf. 7 , 229–245. 10.1007/s12193-013-0118-z [ CrossRef ] [ Google Scholar ]
• Popa-Wagner A., Buga A. M., Tica A. A., Albu C. V. (2014). Perfusion deficits, inflammation and aging precipitate depressive behavior . Biogerontology 15 , 439–448. 10.1007/s10522-014-9516-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ramos S., Garcia Y. F., Anton E., Casaponsa A., Dunabeitia J. A. (2017). Does learning a language in the elderly enhance switching ability? J. Neurolinguistics 43 , 39–48. 10.1016/j.jneuroling.2016.09.001 [ CrossRef ] [ Google Scholar ]
• Reuter-Lorenz P. A., Park D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition . Neuropsychol. Rev. 24 , 355–370. 10.1007/s11065-014-9270-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sanders A. E., Hall C. B., Katz M. J., Lipton R. B. (2012). Non-native language use and risk of incident dementia in the elderly . J. Alzheimers Dis. 29 , 99–108. 10.3233/JAD-2011-111631 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sandu R. E., Buga A. M., Uzoni A., Petcu E. B., Popa-Wagner A. (2015). Neuroinflammation and comorbidities are frequently ignored factors in CNS pathology . Neural Regen. Res. 10 , 1349–1355. 10.4103/1673-5374.165208 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schlegel A. A., Rudelson J. J., Tse P. U. (2012). White matter structure changes as adults learn a second language . J. Cogn. Neurosci. 24 , 1664–1670. 10.1162/jocn_a_00240 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• SeniorsMatter (2017). Learning a language as a senior . Available online at: http://seniorsmatter.com/learning-a-language-as-a-senior/
• Singleton D. M., Lengyel Z. (1995). The Age Factor in Second Language Acquisition: A Critical Look at the Critical Period Hypothesis. Clevedon: Multilingual Matters. [ Google Scholar ]
• Statista (2017). Proportion of selected age groups of world population in 2017, by region . Available online at: https://www.statista.com/statistics/265759/world-population-by-age-and-region/ [Accessed on January 2, 2018].
• Stern Y. (2013). Cognitive reserve: implications for assessment and intervention . Folia Phoniatr. Logop. 65 , 49–54. 10.1159/000353443 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Strauss S. (2015). Does bilingualism delay dementia? CMAJ 187 , E209–E210. 10.1503/cmaj.109-5022 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tyler L. K., Shafto M. A., Randall B., Wright P., Marslen-Wilson W. D., Stamatakis E. A. (2010). Preserving syntactic processing across the adult life span: the modulation of the frontotemporal language system in the context of age-related atrophy . Cereb. Cortex 20 , 352–364. 10.1093/cercor/bhp105 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ware C., Damnee S., Djabelkhir L., Cristancho V., Wu Y. H., Benovici J., et al.. (2017). Maintaining cognitive functioning in healthy seniors with a technology-based foreign language program: a pilot feasibility study . Front. Aging Neurosci. 9 :42. 10.3389/fnagi.2017.00042 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilson R. S., Boyle P. A., Yang J., James B. D., Bennett D. A. (2015). Early life instruction in foreign language and music and incidence of mild cognitive impairment . Neuropsychology 29 , 292–302. 10.1037/neu0000129 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Woumans E., Santens P., Sieben A., Versijpt J., Stevens M., Duyck W. (2015). Bilingualism delays clinical manifestation of Alzheimer’s disease . Biling. Lang. Cogn. 18 , 568–574. 10.1017/s136672891400087x [ CrossRef ] [ Google Scholar ]
• Yeung C. M., St John P. D., Menec V., Tyas S. L. (2014). Is bilingualism associated with a lower risk of dementia in community-living old adults? Cross-sectional and prospective analyses . Alzheimer Dis. Assoc. Disord. 28 , 326–332. 10.1097/WAD.0000000000000019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zahodne L. B., Schofield P. W., Farrell M. T., Stern Y., Manly J. J. (2014). Bilingualism does not alter cognitive decline or dementia risk among Spanish speaking immigrants . Neuropsychology 28 , 238–246. 10.1037/neu0000014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Second Language Learning

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Adult Second Language Acquisition Follow Following
• Children Second Language Acquisiton Follow Following
• Foreign Language Anxiety Follow Following
• Second Language Acquisition Follow Following
• Brain Follow Following
• Willingness to Communicate Follow Following
• Feminisme Follow Following
• Second Language Acquisiton Follow Following
• English Language Studies Follow Following
• MALL (Mobile Assisted Language Learning) Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

A comprehensive bibliometric and content analysis of artificial intelligence in language learning: tracing between the years 2017 and 2023

• Open access
• Published: 01 April 2024
• Volume 57 , article number  107 , ( 2024 )

Cite this article

You have full access to this open access article

• Abdur Rahman 1 ,
• Antony Raj 1   na1 ,
• Prajeesh Tomy 1   na1 &
• Mohamed Sahul Hameed 1   na1  

1 Altmetric

The rising pervasiveness of Artificial Intelligence (AI) has led applied linguists to combine it with language teaching and learning processes. In many cases, such implementation has significantly contributed to the field. The retrospective amount of literature dedicated on the use of AI in language learning (LL) is overwhelming. Thus, the objective of this paper is to map the existing literature on Artificial Intelligence in language learning through bibliometric and content analysis. From the Scopus database, we systematically explored, after keyword refinement, the prevailing literature of AI in LL. After excluding irrelevant articles, we conducted our study with 606 documents published between 2017 and 2023 for further investigation. This review reinforces our understanding by identifying and distilling the relationships between the content, the contributions, and the contributors. The findings of the study show a rising pattern of AI in LL. Along with the metrics of performance analysis, through VOSviewer and R studio (Biblioshiny), our findings uncovered the influential authors, institutions, countries, and the most influential documents in the field. Moreover, we identified 7 clusters and potential areas of related research through keyword analysis. In addition to the bibliographic details, this review aims to elucidate the content of the field. NVivo 14 and Atlas AI were used to perform content analysis to categorize and present the type of AI used in language learning, Language learning factors, and its participants.

Avoid common mistakes on your manuscript.

1 Introduction

Artificial Intelligence (AI) holds a pivotal role in rebuilding our society and ‘promising a new era’ in prospective times for its capabilities to act as intelligent beings in various domains including education (Farrokhnia et al. 2023 ; Górriz et al. 2020 ). An upsurge, in recent times, on the application of AI in educational sectors, has exhibited notable development, and there has been an equivalent explosion in the number of new AI tools accessible (Chu et al. 2022 ; Popenici and Kerr 2017 ). In the field of education, researchers report on the opportunities that AI presents for instructors and learners (Chen et al. 2020 ). This evolutionary trajectory of AI in education is increasing, showcasing an exponential growth of its explorations across disciplines including language education (Jeon et al. 2023 ).

The use of AI, in particular with language learning, is appreciated for providing the students with individualized attention, “personalized, interactive, and authentic language learning contexts” in the form of intelligent tools such as Interactive Personal Assistants, web-based systems, virtual reality systems, or chatbots (Lin and Chang 2020 ; Liang et al. 2021 ; Wijekumar et al. 2013 ; Rahman and Tomy 2023 ; Zhang et al. 2023 ). Moreover, it allows the teachers/instructors to monitor their students/learners, which reduces their workload and frees the teachers thus allowing them to prioritize the curriculum over repetitive tasks (Pokrivčáková 2019 ). Integration of AI techniques such as natural language processing (NLP), natural language understanding (NLU), and automatic speech recognition (ASR) allows the use of tools developed through them to be more appropriate in language learning platforms as they comprehend and process human-computer interaction (Lee and Jeon 2022 ; Shadiev and Liu 2022 ).

While multiple studies address the research gaps in using AI in language learning, the advancement of AI is at a much faster phase extending the research need perpetually. Gruetzemacher ( 2022 ) states that “In the past two years, Language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do”. The technological impediments of the past decades are no longer the present-day concerns. Therefore, the research trends and findings of previous studies slither on every massive leap of AI techniques. For example, in 2017, advancements in word embedding (Peters et al. 2017 ), and the introduction of Transformer model architecture in the field of NLP outperformed prior recurrent neural network models and offered a novel technique to carry out sequence transduction tasks (Vaswani et al. 2017 ). Later, the launch of ChatGPT and GPT-4 in 2022 and 2023 offered remarkable conversational capabilities and longer text processing demonstrating keen anticipation for NLPs and the next phase of human-computer interaction (Weitzman 2023 ). Such advancements of NLP greatly help intelligent systems uncover the unstructured data produced by humans.

The production and reception of natural language consequently expand the use of AI for language learning. Past studies have examined these phenomena of AI in language learning and education. For instance, Liang et al. ( 2021 ) performed a bibliographic analysis and systematic review on 71 articles on AILEd (AI in language education) on December 31, 2020. Even though research on language teaching and learning in connection with AI is active, given the dynamic progressive nature of AI techniques, reviewing their implications and applications on language learning, within the designated timeframe, could considerably contribute to the field. Hence, it is essential to state the art of AI in language learning at timely intervals. To review the state-of-art of AI in language learning, a bibliometric analysis was performed. This analysis could segment a “large volume of scientific publications” with the “advancement, availability, and accessibility of bibliometric software and scientific databases”, (Donthu et al. 2021 ). It could objectively point out the performance and emerging trends in the given field including topics and authors (Verma and Gustafsson 2020 ). With the study’s primary objective being to review the developments in academic research on AI and language learning between the years 2017 and 2023, the study focuses (1) to analyze the publication trends and growth patterns of AI in language learning (2) to identify the key contributors, collaboration patterns, and influential works in the field (3) to explore the dominant research themes and emerging trends. To operationalize the objectives of the research, they were converted into the following research questions to identify the mentioned publication metrics.

1.1 Research questions

RQ1: What are the publication trends and metrics of performance analysis such as Publication, Citation, and both Citation and publication-related metrics?

RQ2: Who or Which are the most influential and productive authors, institutions, journals, and countries?

RQ3: What are the key research themes, frequent and prominent keywords obtained from title, abstract, and keywords through keywords analysis?

RQ4: What are the documents and clusters that are connected to a common document’s reference through bibliographic coupling?

RQ5: what are the inferences obtained by analyzing the content of all the documents in the study through content analysis? With the study’s primary objective being to review the developments in academic research on AI and language learning between the years 2017 and 2023, the study focuses (1) to analyze the publication trends and growth patterns of AI in language learning (2) to identify the key contributors, bibliographic clusters, and influential works in the field (3) to explore the dominant research themes and emerging trends. To operationalize the objectives of the research, they were converted into research questions to identify the mentioned publication metrics. (RQ1) provides valuable insights into the growth and impact of this interdisciplinary domain. This information can guide researchers, policymakers, and educators in identifying areas of prominence and potential gaps in the literature. (RQ2) allows us to recognize key contributors to the field and potentially foster collaborations. Additionally, (RQ3) helps in analyzing key research themes and prominent keywords to comprehend the evolving discourse and focus of research in this area. Moreover, exploring document connections through bibliographic coupling (RQ4) aids in mapping the intellectual structure of the field. Lastly, extracting inferences through content analysis (RQ5) offers insights into the practical implications of the existing research, potentially informing pedagogical tools used, their frequency of usage, the target learners and also the language learning factors. In sum, addressing these research questions not only contributes to the scholarly understanding of this domain but also has practical implications for educators, researchers, and stakeholders invested in the intersection of AI and language learning.

2 Background of the study

2.1 artificial intelligence.

While multiple researchers have laid out technical definitions to bind AI within a school of thought, Russell and Norvig ( 2010 ) categorizes these definitions under two dimensions. First, it has the ability to imitate, think and act humanly and rationally. Second, its connection with the thought process and reasoning along with its behavior. In a broader context, AI involves computing technology that allows machines to mimic human intelligence “in analysis, reasoning, decision making, and self-correction” (Liang et al. 2021 ; Pokrivčáková 2019 ). To perform the above-mentioned operations, a wide variety of techniques and methods are used such as “machine learning, adaptive learning, natural language processing, data mining, crowdsourcing, neural networks or an algorithm” (Pokrivčáková 2019 ).

Despite its complicated mechanisms and progressive developments, IBM (n.d) states that “there is no practical examples of strong AI in use today”. However, as given in Table  1 , AI has had many technological breakthroughs from computer vision to advanced natural language processing techniques over a brief period and it is, in many cases, being considered a substitute for human intelligence. At its present rate of growth, AI is presumed to surpass human intelligence. Thus, a review of its applications in various domains is a pressing priority.

2.2 Integration of artificial intelligence in language learning

The incorporation of AI in any field can be in multiple technological forms, tools, or software (Thayyib et al. 2023 ). Similarly, in language learning and acquisition, a diverse assortment of tools is being integrated with artificial intelligence as it offers language teachers and learners “personalized, interactive and adaptive learning experiences that cater to individual’ needs and preferences” (Rusmiyanto et al. 2023 ; Pokrivčáková 2019 ). Research is being carried out to identify appropriate AI-assisted tools to improve each language skill (Rahman et al. 2022 ) and on the integration of each tool to develop specific areas of language learning and acquisition.

The possibilities of advanced technological input through AI have made Computer Assisted Language Learning (CALL) conventional. Researchers argued that CALL had been limited when proposed with activities directed towards communication and interaction between students such as role plays, discussions, and sharing opinions (Amaral and Meurers 2011 ). The notion of computers replacing humans in CALL was skeptical as language learning reinforces negotiating meaning and having real-time conversations. Later, the increased possibility of human-like interaction with computers through AI techniques, made Intelligent Computer Assisted Language Learning (ICALL) gain its state as a potential computing technology that reformed CALL through its dynamics in multiple aspects (Segler et al. 2002 ; Esit 2011 ; Amaral and Meurers 2011 ). However, not all AI-based tools are directly associated with ICALL, they are diversified into multiple forms and tools as follows; Adaptive Educational System (Triantafillou et al. 2003 ), Intelligent Educational System (Cumming et al. 1993 ) Intelligent Personal Assistant (Rahman and Tomy 2023 ; Yang et al. 2022 ) Intelligent Tutoring System (Slavuj et al. 2015 ), Natural language processing (Nagata 2013 ), Machine translation tools (Briggs 2018 ), Chatbots (Jeon 2021 ; Dokukina and Gumanova 2020 ), AI writing assistants (Gayed et al. 2022 ; Godwin-Jones 2022 ), AI-powered language learning software (Pokrivčáková 2019 ) and Intelligent Virtual Reality (Ma 2021 ). These AI-powered language learning tools are used to foster students’ language skills and sub-skills, encourage students’ interaction, reduce the affective factors of language learning and acquisition, push their willingness to communicate, and more (Tai and Chen 2020 ; Shazly 2021 ; Liang et al. 2021 ). Acknowledging its efficient performance in multiple studies, researchers and applied linguists are paying attention to AI-empowered tools and their applications.

2.3 Previous reviews on AI in language learning and education

Like given in Table  2 , previous review papers have analyzed the trends and research foci of artificial intelligence and AI-powered tools in language learning and education. Review papers have focused on specified AI-based tools like Intelligent tutoring systems, Voice based virtual agents and speech recognition chatbots (Xu et al. 2019 ; Katsarou et al. 2023 ; Jeon et al. 2023 ) and have worked on reviewing the integration of intelligent tools such as ChatGPT and conversational AI, and approaches like ICALL in language learning (Kohnke et al. 2023 ; Weng and Chiu 2023 ; Ji et al. 2022 ). Likewise, researchers have reviewed the role of AI in language learning and language education(Huang et al. 2021 ; Liang et al. 2021 ; Fang et al. 2023 ; Ali 2020 ; Sharadgah and Sa’di 2022 ; Yang et al. 2022 ). Although studies have focused on AI and its applications, reviews on AI, in many cases, have focused either on “Artificial Intelligence” or on other applications such as “Intelligent Tutoring Systems” but not on both. Studies analyzing both aspects are notably limited. Thus, this paper aims to perform an analysis of both aspects which includes AI and AI-powered tools and approaches. Additionally, following the introduction of advanced NLP models in 2022, there has been a notable absence of dedicated reviews concerning the role of AI in language learning. Thus, this study aims to analyze the trends and impact of AI in language learning up to the year 2023. This includes the most advanced ChatGPT and other NLP-powered intelligent agents. Along with bibliometric analysis, through content analysis we also investigated its connectives with language learning factors and the target learners through the mode of instruction.

3 Methodology

3.1 defining aims and scope.

It is essential to set clear objectives and parameters before moving on to the process of bibliometric analysis Belmonte et al. ( 2020 ); Donthu et al. ( 2021 ). The authors sought to analyze the conceptual framework and reflect on influential research contributors and their collaboration in the research area. Any area of research with more than 500 papers “deserves a bibliometric analysis” (Hou and Yu 2023 ; Donthu et al. 2021 ). The scope of the study, in accordance with the regular standards, will analyze more than 500 papers. Along with bibliometric analysis, a content analysis will be performed to identify the key applications and their participants in the research area.

PRISMA method procedure for screening and selecting the documents

3.2 Data source

The authors opted for the Scopus database to carry out the bibliometric and content analysis, in line with other prior bibliometric research (Thayyib et al. 2023 ; Ahmed et al. 2022 ; Goodell et al. 2021 ). Scopus is one of the largest databases of scholarly works with more than 84 million records and 1.8 billion cited references (Home https://hai.stanford.edu/ ). With its advanced search capabilities and wide coverage, it holds the records of research works being published even in developing countries. It offers adequate bibliometric details such as citation information, bibliographic information, abstracts, keywords, funding details, and other information including references. In addition, Scopus provides these data in multiple formats to feed into software that is used to systematically analyze documents.

3.3 Data collection and refinement

The authors retrieved the data used for analysis from the Scopus database on June 22, 2023. The period range was limited between the years of 2017 and 2023 to obtain scholarly works that mostly reflect advanced AI techniques in language learning. With reference to previous relevant bibliometric and systematic reviews, the search keywords were finalized (Hou and Yu 2023 ; Liang et al. 2021 ; Popenici and Kerr 2017 ; Chu et al. 2022 ; Jeon et al. 2023 ; Tan et al. 2022 ). To further extend the scope of the study, ChatGPT was also included. However, we excluded “machine learning”, “deep learning” and “deep neural networks”. While undoubtedly, these components are important in the broader field of AI, these terms tend to yield a substantial number of papers related to computer language learning and programming languages, which are distinct from our primary focus on second language learning.

The chosen keywords were influenced by the prominent pedagogical viewpoint within second language learning. NLP techniques, conversational systems, and interactive chatbots are frequently employed in this context to facilitate meaningful interactions between the AI and the learner. Considering these factors, the following keywords were used to search relevant articles in the database ( TITLE-ABS-KEY ( "Chatbot*" OR "conversational agent" OR "pedagogical agent" OR "conversational system" OR "dialogue system" OR "spoken dialogue system" OR "intelligent personal assistant" OR "ICALL" OR "intelligent computer assisted language learning" OR "artificial intelligence" OR "intelligent tutoring system" OR "ChatGPT" OR "ChatGPT-4" OR "natural language processing" OR "NLP" ) AND TITLE-ABS-KEY ( "Language learning" OR "language teaching" OR "language acquisition" OR "second language learning" OR "foreign language learning" ) ). A total of 1870 results were obtained from the keyword search. The publication selection procedure is given in Fig.  1 Page et al. ( 2021 ), and the inclusion and exclusion criteria are in Table  3 . On the exclusion of articles based on the criteria, only 606 documents were processed for bibliometric and content analysis.

3.4 Technical tools and procedure of data analysis

Bibliometric analysis and content analysis were performed to gain an overall understanding of the research on AI in language learning. The study employed bibliometric analysis to identify the publication trends, leading authors, institutions, prominent journals, collaboration patterns, citation analysis, geographic distribution, keywords co-occurrence analysis, co-authorship analysis, and co-citation analysis. The authors used VOSviewer, Publish or Perish software, and Scopus to visualize and extract results from the retrieved data with the objective to carry out bibliometric analysis.

The authors, through content analysis, opted to identify the overview of the types of AI tools used in language learning, the language skills it is being tested against, and the educational levels of the participants of the study. After identifying the keywords through text-based content analysis on VOSviewer, a conceptual content analysis was performed, adhering to the deductive coding approach following the structural coding method through code categorization (Krippendorff 2018 ). The coding scheme for content analysis was done with NVivo, which assists in “classifying, sorting and modelling qualitative data” (Bazeley 2019 ). The schemes of the documents were initially classified through auto-coding in NVivo. In addition, manual classification was done independently by two research scholars to examine the auto-coded results. The auto-coding was fed into Atlas AI to identify the connections between the variables of the study.

4.1 Publication trends and performance analysis of AI in language learning

The screened data had 606 documents published between the years 2017 and 2023. It included 230 research articles, 29 book chapters, 330 conference papers, and 17 reviews. Among these documents, 39 were articles-in-press. Most of these documents were closed access, only 185 articles were open access among which 117 were research articles, 61 were conference papers, and 7 were reviews. However, along with the increase in the total number of documents published over time, open-access documents got doubled between 2017 and 2023. As shown in Fig.  2 , there is a noticeable growth in the total number of documents published from 2017 to 2023 in the subject area. From 2017 to 2022, there has been a gradual increase in the number of documents that were published, indicating a growth of 189.8 \(\%\) . The data presented for 2023 is not complete as it was collected during the middle of the year (June 22, 2023). However, the reported number of 72 documents signifies promising growth. While the number of research publications has increased, the number of citations has decreased over time as presented in Fig.  2 .

Publications and citations between 2017 and 2023

“Performance analysis examines the contribution of research constituents to a given field” (Donthu et al. 2021 ). With total publication and total citation details, the analysis has other metrics to be evaluated including ‘scientific actors’ like h-index and i-index (Cobo et al. 2011 ). Table  4 indicates the overall performance of AI in language learning through selected metrics from Donthu et al. ( 2021 ).

Donthu et al. ( 2021 ) states that it is a “standard practice” to present the background or profile of the retrieved documents. Thus, the study further elaborates on the contributions of the (1) authors, (2) institutions, (3) sources, and (4) countries that are highly influential in the field of AI in language learning. The following formulas were used to calculate the metrics identified in Table  4 : PAY = (TP \(\div\) NAY), ACP = TC \(\div\) TP, PCP = (NCP / TP) * 100, and CCP = (TC \(\div\) NCP). NCA was calculated by identifying (The total number of authors - Duplicates) in Microsoft Excel. NAY is the total number of years that the research constituent records the publications and NCP was identified by filtering the publications with citation in Microsoft Excel. The h-index and I- Index were calculated through Publish or Perish software by the RIS format derived from Scopus (Table 4 ).

4.2 Top authors, sources, countries, and institutions

We used VOSviewer and R studio (Biblioshiny) to identify and cross-validate individual influential authors through co-citation analysis. As given in Table  5 , the authors identified are Meurers d.; Fryer I.K.; Hwang g.j.; Dizon. G.; Chen x.; Zou d.; Strik. H; Cucchiarini c.; Thompson A.; and Xie h. In Table  6 , the top ten cited sources with a minimum of 5 documents in the field are listed. The sources are Computer Assisted Language Learning, Interactive Learning Environments, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Educational Technology and Society, Procedia Computer Science, ACM International Conference Proceeding Series, Journal of Physics: Conference Series, Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2018 at the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HTL 2018, International Journal of Emerging Technologies in Learning, Applied Sciences (Switzerland). The document type of sources are Journals, Book series, and Conference proceedings. Table  7 includes documents and citations of the top ten organizations with high impact in the field of AI in language learning between the years 2017 and 2023. The University of Sydney, Himeji Dokkyō University, National Taiwan Normal University, University of Piraeus, Georgia State University, National Taiwan University of Science and Technology, University of Tübingen, The Education University of Hong Kong, Lingnan University and the University of Cambridge are the most influential organisations.

Influential countries

Figure  3 presents the top ten influential countries in the field along with their total number of documents produced and citations achieved. We found out that the United States, Japan, China, Hong Kong, Taiwan, Canada, Germany, Australia, the United Kingdom, and Indonesia are the most productive and influential countries. VOSviewer was used to identify these countries by evaluating the citations against countries. The minimum number of documents per country was set to 10 to identify countries that are both productive and influential.

4.3 Keywords analysis

Co-occurrence of keywords visualization

The co-occurrences of keywords analysis was made through VOSviewer to identify the keywords that authors use frequently. With full counting, the co-occurrence of the author keywords has opted with keyword occurrences of \(>5\) . Of the 3252 keywords, only 184 (5.66 \(\%\) ) met the threshold with 4069 links and a total link strength of 10134. The overall strength of the co-occurrence connections between each of the 184 keywords was calculated. By doing so, Fig.  4 was generated.

The 184 keywords were classified into seven clusters. Table 8 details the seven clusters. Cluster 1 consists of 49 items, like language learning (f = 193, Total link strength = 1026), Natural language processing systems (f = 132, Total link strength = 817), Computational linguistics (f = 46, Total link strength = 262), Second language acquisition (f = 44, Total link strength = 207), and Deep Learning (f = 32, Total link strength = 231). Cluster 2 comprised 43 items, including Artificial Intelligence (f = 211, Total link strength = 1051), Students (f = 112, Total link strength = 784), Teaching (f = 94, Total link strength = 664), Engineering Education (f = 49, Total link strength = 397), and Education computing (f = 39, Total link strength = 317). Cluster 3 has 43 items and the most occurred keywords in this cluster are E-learning (f = 78, Total link strength = 556), linguistics (f = 39, Total link strength = 283), Education (f = 27, Total link strength = 175), Chatbot (f = 26, Total link strength = 92), and English languages (f = 24, Total link strength = 183). Cluster 4 consists of 16 items, including Learning systems (f = 125, Total link strength = 896), Foreign language (f = 36, Total link strength = 238), Computer Assisted Language Learning (f = 28, Total link strength = 195), Intelligent computer-assisted language learning (f = 16, Total link strength = 61), and Error correction (f = 10, Total link strength = 62). 14 items were found in cluster 5, like Computer aided instruction (f = 109, Total link strength = 824), Intelligent tutoring systems (f = 25, Total link strength = 178), Foreign language learning (f = 24, Total link strength = 142), Educational technology (f = 13, Total link strength = 79), and Tutoring system (f = 10, Total link strength = 85). Cluster 6 comprises 11 items including Speech recognition (f = 24, Total link strength = 181), Automatic speech recognition (f = 8, Total link strength = 70), Machine translations (f = 9, Total link strength = 62), Computer-aided language translation (f = 7, Total link strength = 57), and Deep neural networks (f = 7, Total link strength = 48). In Cluster 7, 8 items were found including Teacher (f = 27, Total link strength = 212), Learning platform (f = 8, Total link strength = 48), Decision making (f = 6, Total link strength = 44), Statistical tests (f = 5, Total link strength = 35), and online learning (f = 5, Total link strength = 32).

4.4 Bibliographic coupling

Bibliographic coupling based on Documents

Bibliographic coupling was done to identify literature that is connected through a common document’s reference. The network visualization of the documents as illustrated in Fig.  5 details the interconnections between the documents through 5 clusters. To perform this visualisation, full counting was opted with the unit of analysis as Bibliometric coupling with documents. In order to narrow down the influential works, the minimum number of citations per document was set to 20. Of the 606 documents, only 40 met the threshold. Among the filtered 40 documents, only 21 items in the network had the largest set of connected items.The most influential authors of AI in language learning are listed in Table  9 .

The most influential documents identified through Bibliometric coupling are “Stimulating and sustaining interest in a Language Course: An experimental comparison of Chatbot and Human task partners” (Fryer et al. 2017 ), “Technology and the Future of language teaching” (Kessler 2018 ), “Chatbot learning partners: Connecting learning experiences, interest, and competence” (Fryer et al. 2019 ), and “Using Intelligent Personal Assistants for Second Language Learning: A Case Study of Alexa” Dizon ( 2017 ), and “Chatbots for language learning-Are they really useful? A systematic review of chatbot-supported language learning” (Huang et al. 2021 ).

4.5 Content analysis

AI tools and techniques

The title and the abstract fields of all the documents were fed into VOSviewer to trace out the most occurred words. With a minimum number of occurrences per term set to 10, we identified 382 terms. For each of the 382 items, a relevance score was calculated by default in VOSviewer, and only 60 \(\%\) of the most relevant terms were opted for further analysis. Upon filtration, we created a map based on textual data with 299 items under 6 clusters. Among these 299 items from VOSviewer, we excluded terms that do not add up to any contextual meaning such as research gap, participants, perception, English language teaching, methodology, experiment results, observation, sample, control group, experimental group, the current study, post-test, and questionnaire. A total number of 254 were excluded on such pretest. A total number of 45 terms were manually coded through NVivo 14 into three different schemes or parent codes namely (1) Artificial Intelligence Tools and Techniques (2) Participants (3) Language Learning Factors.

AI tools and techniques gave us an overview on the type of AI-based technology that is implemented in the studies, the participants’ parent code had the different age groups of learners upon whom the experiments had been conducted, and the Language Learning Factors parents code had the factors with which the AI techniques were tested against. Figure  6 illustrates the 45 items under three different clusters. The child codes are organized under parent codes. The terms used most frequently under AI tools and techniques are as follows with their occurrence in references: AI Chatbots (f = 521), ChatGPT (f = 22), Conversational Agent (f = 217), Automatic Speech Recognition (f = 216), Intelligent Personal Assistants (f = 216), Google Assistant (f = 46), Amazon Alexa (f = 24), Agent (f = 209), Virtual Reality (f = 171), Natural Language Processing (f = 170), CALL (f = 152), ICALL (f = 79), Machine Translation (f = 134), Web (f = 108), Application (f = 94), Intelligent Tutoring System (f = 60), Robot (f = 45), Error Correction (f = 43), Gengobot (f = 40), MALL (f = 39), Mobile Devices (f = 15), Mobile Learning (f = 12), Mobile Applications (f = 8), ICT (f = 29), and Gamification (f = 28). The terms most frequently used under the parent code Language Learning Factors are Writing (f = 184), Speaking (f = 153), Vocabulary (f = 148), Listening (f = 106), Grammar (f = 92), Proficiency (f = 81), Accuracy (f = 79), Pronunciation (f = 71), Fluency (f = 66), Motivation (f = 64), Sentence pattern (f = 61), Comprehension (f = 57), Anxiety (f = 32), and Formulaic sequence (f = 22). Third, the terms most frequently used under the parent code of participants are University students (f = 116), Children (f = 100), College students (f = 85), Language teachers (f = 65), Higher education (f = 63), and School students (f = 18).

Two field plots of the relationships

Then, a code-occurrence analysis was conducted to present a two-field plot to depict the relationship between the participants and the AI tools and techniques used and between language learning factors and AI tools and techniques. In order to do that, we fed the parent and child codes into ATLAS AI to generate the visualizations in Fig.  7 and to identify the link strength between the parent and its sub-codes. The plot showcases the co-occurrence patterns between the two variables. The dense clusters illustrated in Fig.  7 b indicate strong link strength such as the link between Teachers and chatbots and between chatbots and grammar in Fig.  7 a. Though it depicts the association between the tested variables, not all the variables that were fed were displayed due to their weak association such as ChatGPT in Fig.  7 a and formulaic sequences in Fig.  7 b.

5 Discussion

RQ1: What are the publication trends and metrics of performance analysis such as Publication, Citation, and both Citation and Publication-related metrics?

RQ1 is devoted to identifying the research trend of AI in language learning. The annual total publication and citation records could provide an overview of the future of the research area and its potential. The findings of publication trends given in Fig.  2 depict a gradual growth in terms of production (publication) till 2022. As the data was collected on June 22, 2023, the production rate is still incomplete. However, a promising amount of literature has been produced within the first half of the year. In contrast to the rising publication rates, a decrease in citation records could be observed. The decrease in citations may have been attributed to the research focus shift. Over time, researchers have been exploring new AI-based technologies that have not gained much attention. Researchers have shifted focus from generic terms such as ”Artificial Intelligence” to specific tools such as IPA and ChatGPT. In both cases, the need for researchers to cite other specific applications and tools is low. The plausible reason for the reduction in citations could be the saturation of the field. The growth may have reached a point where new papers are not cited as frequently as old papers which are considered to be foundational works. We further analyzed various other metrics of publication and citation to gain insights into AI in LL. The findings shed light on productivity, its impact, and the rate of collaboration in the field. With the total number of included publications, sole-authored publications, and co-authored publications, we evaluated the level of collaboration among the researchers. The results revealed that the ( CI = 0.76) indicated a strong culture of collaboration among the researchers. The productivity of AI in language learning (TP = 606; PAY = 87) is on par with other renowned bibliometric or systematic reviews on pedagogic techniques in language learning like Virtual tools with (TP = 104), Mobile assisted vocabulary learning with (TP = 687), synchronous computer-mediated communication with (TP = 1292), and Augmented Reality with (TP = 1275) despite excluding documents published before 2017 (Botero-Gomez et al. 2023 ; Daǧdeler 2023 ; Hou and Yu 2023 ; Min and Yu 2023 ).

The research output of AI in LL has received a total of 3194 citations, with an average of 5.27 citations per document and an ACY of 456.28, indicating that the academic community has a positive reception of the produced documents. Other citation metrics such as CCP and PCP evaluate the amount and the impact of the influential works of the field. Notably, more than 60 \(\%\) of the documents had citation records with an average CCP of 8.72, suggesting a high percentage of influential papers in the field. We further examined the research impact indices to reflect the overall impact of authors in the field. The (h-index = 27, G = index = 41, and I10 index = 85) for our dataset suggests that the scholars in our field have had a significant impact.

The second research question aims at identifying the top authors, institutions, countries, and journals. The results of the analysis provided valuable insights into the contributors to the field. Our study revealed prolific authors who have made a major contribution to the field of AI in LL. The identified authors have contributed 30 documents altogether with a strong collaboration pattern between each other and other authors of the field. Only 10 \(\%\) of the 30 documents were sole-authored publications, and 90 \(\%\) being collaborative contributions. Moreover, it was found that 30 \(\%\) of the documents produced by these authors demonstrated collaboration among themselves.

The authors focused mostly on discussing the general trends and problems surrounding AI (Huang et al. 2023 ; Chen et al. 2021 ; Liang et al. 2021 ) and its tools such as Chatbots (Zhang et al. 2023 ; Huang et al. 2021 ; Fryer et al. 2019 ), ChatGPT (Kohnke et al. 2023 ), IPAs like Alexa and Google Assistant (Dizon et al. 2022 ; Dizon 2021 ), ICALL (Chen et al. 2022 ; Ruiz et al. 2019 ), Grammarly (Dizon 2021 ), Natural Language Processing (Ziegler et al. 2017 ), Speech (Litman et al. 2018 ) and digital technologies (Liu et al. 2023 ; Kienberger et al. 2022 ). While influential authors play an indelible mark in any field, equally remarkable is the role of academic sources who support the research fields. In the field of AI in LL, the most influential sources are journals (5) followed by conference proceedings (4) and book series (1). The journals have produced ( \({\bar{x}}\) = 8.6, SD = 3.36, Min = 5, Max = 13) documents with the impact measured through citation of ( \({\bar{x}}\) = 93.2, SD = 50.74, Min = 30, Max = 166) between 2017 and 2023. The renowned journals of the field publish articles about AI in LL under the categories of language and linguistics, computer science applications, and education which indicates that the research field is multidisciplinary and not bound to any particular school of thought.

In accordance with the knowledge gained from influential journals, organizations that publish influential works include not only the Department of English Language Education but also interdisciplinary departments such as Institute of Technology, Institutes of Digital Learning and Education, Department of Mathematics and Information Technology, Department of informatics, Computer Science Department, Institutes of Automated Language Teaching and Assessment, and Institute and Department of Computer Science and Technology. Thus, maintaining a highly multidisciplinary approach in the field. On the other hand, when we looked at the most productive and impactful countries in the field, we found that the majority of 156 documents on AI came from China, and the United States has got the highest number of citations of 778 with 78 documents. The results of our study are consistent with other bibliometric studies that link AI with other sectors, such as Big Data Analytics (Thayyib et al. 2023 ), Food Safety (Liu et al. 2023 ), and Smart Buildings (Luo 2022 ), although a similar study on the role of AI in language education claimed that Taiwan, the US, and the United Kingdom had secluded the top most spots. The prior analysis by Liang et al. ( 2021 ) examined documents between 1889 and 2020, whereas our study looked at documents published between 2017 and 2023, which may have led to a difference in our results. Thus, the overall analysis of RQ2 provides insights into the most influential authors, institutions, sources, and countries which can guide researchers to understand the factors that contribute to their success.

RQ3: What are the key research themes, frequent and prominent keywords obtained from title, abstract, and keywords through keyword analysis?

RQ3 illustrated extensively used keywords of AI in LL. The authors merely listed the keywords that were automatically retrieved and clustered by VOSviewer. The list contains highly occurred keywords with strong TLS and occurrences. Results reveal that “Natural language processing systems” is the most occurred technical keyword apart from “Artificial Intelligence”. However, contextual meaning or research inferences could not be obtained through the use of NLP in language learning as most AI-based systems used in language learning and acquisition platforms uses tools that are incorporated with NLP (Meurers 2012 ; Zilio et al. 2017 ). But hints for future studies could be obtained through keywords that have lower TLS and occurrences. The identified keywords with weaker connections could be focused by the researchers if found to be potential areas of research.

In RQ4, documents that were often cited by other authors of the same field were identified through bibliographic coupling. The top documents identified through bibliometric coupling were published in the year 2017 followed by 2018 and 2020. According to Dogan et al. ( 2023 ), a significant amount of literature was produced on AI in education in 2018. Our study, which aligns closely with Dogan’s findings, also observed a similar pattern, with a high number of influential works published in 2017, followed by another peak in 2018. The use of AI, Chatbots, and Alexa are discussed in most documents (Fryer et al. 2017 ; Dizon 2017 ; Huang et al. 2021 ). These documents are seen, in most cases, to be foundational works, which could be the cause of the declining citation patterns as discussed in RQ1. The inferences obtained through bibliometric coupling identify the key papers and shed light on the research landscape.

Hierarchical chart of the content analysis through schematic coding

RQ5: what are the inferences obtained by analyzing the content of all the documents in the study through content analysis?

RQ5 aimed at quantitatively contextualizing the content by coding the documents into clusters and therefore deducing inferences. Figure  8 illustrates the types of AI used in language learning, the language learning factors, and its participants based on hierarchy compared by a number of coding references. According to the model given, a large number of studies have been conducted with AI-embedded Chatbots. In line with the aforementioned statement, Jeon et al. ( 2023 ) conducted a systematic review of chatbots acknowledging their widespread application. Like Chatbots, other AI tools like CALL, Conversational agents, Virtual Reality, NLP tools, and IPAs are prevalent in the field. On the other hand, writing is the language learning factor that is mostly preferred with AI applications in language studies followed by speaking, vocabulary, proficiency, accuracy, pronunciation, listening, and fluency.

The participants that are most sought after for implying AI are university students followed by children, college students, language teachers, and students of higher education. In addition to figuring out the dominant components within the variables, we examined the interconnections among them to establish previously established research areas and research gaps. Chatbot was experimented extensively with teachers, children, and university students. Figure  7 b depicts the relationship between AI tools and the participants. Even though a high amount of interconnections could be observed with different language learning factors and AI tools, the interconnections between AI tools and the levels of participants are weak. Future studies could work on experimenting with AI tools with different participant levels. Despite the fact that there is a lot of literature on writing skills, many AI tools have been tested with speaking skills. Researchers could contribute to the field by working on weaker connections. For instance, students in colleges, universities, and schools might be exposed to different AI tools. The same could be done for fluency and anxiety, which are core areas of research with weaker connectives.

6 Conclusion

This study used bibliometric and content analysis to analyze the research trends, patterns, key contributors and content in the field of AI in LL. It summarises the bibliometric information of the field along with prominent authors, institutions, sources, and countries. A rise in publication trends has been identified. Researchers who integrate AI into language learning use a variety of tools, leading to the formation of new fields within the field and new branches within AI-based language learning. This, in turn, is speculated to be a major reason for the decline in citation trends. However, the constructive viewpoint regarding this aspect is that the researchers, utilizing diverse AI-based tools, are expected to contribute significantly to the field. Affirming this, documents that were published during 2017 and 2018 are identified, through bibliographic coupling, to be ‘often cited’ papers indicating their mark as foundational works. On analysing the bibliographic and textual data on multiple aspects, we yielded the following results:

Between 2017 and 2022, there is a considerable increase in the number of publications on AI in language learning of 189.8 \(\%\) demonstrating a promising growth in the field with 60.3 \(\%\) of the documents with citations of ( \(\ge 1\) ).

The field exhibits a significant number of co-authored publications, totalling to 466, in contrast to the relatively lower count of sole-authored publications, which stands at only 140.

As the field is emerging, a lot of new tools and technologies are being incorporated into the field. Resulting in a high number of citations for the works published in 2017 and 2018. The articles published during this period are often cited and considered as foundational works.

Our findings have identified the United States, China, and Japan, sequentially, as the most influential countries in publishing research related to AI in language learning.

On analysing the author’s keywords, we identified that there is an upsurge in the following in areas of study in connection with AI in language learning: natural language processing, computational linguistics, deep learning, speech recognition, machine translation, and deep neural networks. Among these keywords, “Natural language processing” is the most used keyword indicating the presence of Large Language Models of AI being frequently opted in language studies.

Fryer et al. ( 2017 ) and Kessler ( 2018 ), which examine the usage of chatbots in language learning and the impact of technology including AI in language learning, consequently, were shown to be the most influential texts. The first document talks about the usage of Chatbots in language learning setup, and the second document discusses the extensive use of technology in language learning. This conclusion, through bibliographic coupling, is consistent with the outcomes of the content analysis.

Through content analysis, we identified the most occurring textual terms used in the retrieved data from Scopus. We identified that the most occurring AI tool was Chatbots followed by Chatgpt, Conversational Agents, Automatic Speech Recognition and Intelligent Personal Assistants. We also identified the most researched language learning factor with AI which is Writing followed by Speaking, Vocabulary, Listening and Grammar. The most targeted participants are University Students followed by children, college students and language teachers.

6.1 Implications and contributions

In light of the rapid pace of technological advancements, several reviews are limited to incorporating the latest NLP tools, such as ChatGPT and Intelligent Personal Assistants, into their analyses. While prior bibliometric analyses have provided us with a comprehensive understanding of the bibliometric landscape concerning AI in language learning, the dynamic nature of AI necessitates an investigation inclusive of the recently launched tools, particularly in light of the technological developments emerging post-2019, with ChatGPT serving as a prime illustration thereof. Surprisingly, no prior bibliometric analysis has embraced these cutting-edge NLP tools and techniques. Furthermore, there exists a conspicuous absence of content analysis within the domain of AI in language learning. Hence, our study aims to bridge these critical gaps by providing a thorough examination of the wide range of tools and techniques utilized in AI for language learning, their respective frequencies, and the target participant groups they have been applied to. The outcomes of our research will enable future researchers to identify research gaps through content analysis by providing them with a comprehensive understanding of bibliometric information. The frequency of research among the three factors of content analysis will also serve as a vital resource for pinpointing areas that needs research attention.

6.2 Limitation of the study and recommendation for future studies

Through addressing the limitation of the study, we would want to suggest areas for further research. First, the study is limited to the Scopus database and between the years 2017 and 2023. Despite Scopus being an academically promising database for language studies, documents published on the Web of Science, ERIC, ScienceDirect and Google Scholar could be paid due attention to extend the coverage. The conclusive decision of both the databases shall reflect well the research field. Thus, the findings of the bibliometric and content analysis are limited to the Scopus database. Though some documents are indexed in more than one database, the inclusion of any of the databases could alter the findings of the bibliometric findings. Second, Even though we included most AI tools and techniques including ChatGPT and IPAs, not every tool was included in this study. keywords refinement can be done to identify more papers addressing other AI tools in the field. For instance, keywords identified through this study such as “Deep learning”, “machine learning”, “deep neural network”, “Machine translation” and “computer-aided instruction” could be included in future studies to extend the scope of the field. This study acknowledges the assumption that the identified keywords and content categories faithfully reflect the diversity and intricacies of AI tools employed in language learning. However, it is important to recognise that this approach may inadvertently overlook emerging trends or unconventional terminologies within this rapidly evolving field. Future research endeavors should remain attuned to these evolving nuances in the realm of AI tools for language learning. A different approach to review shall also be considered. This study has applied quantitative analysis to examine the research scope, similarly, studies could opt for qualitative analysis to extract valuable insights. Systematic reviews can be done on other prominent AI tools identified through content analysis. Given these limitations, the findings of the study can be beneficial for researchers in the field of AI in LL, since the study outlines both the research focus and the research gaps.

Ahmed S, Alshater MM, Ammari AE, Hammami H (2022) Artificial intelligence and machine learning in finance: a bibliometric review. Res Int Bus Financ 61:101646. https://doi.org/10.1016/j.ribaf.2022.101646

Article   Google Scholar  

Ali Z (2020) Artificial intelligence (AI): a review of its uses in language teaching and learning. IOP Conf Ser 769(1):012043. https://doi.org/10.1088/1757-899x/769/1/012043

Amaral L, Meurers D (2011) On using intelligent computer-assisted language learning in real-life foreign language teaching and learning. ReCALL 23(1):4–24. https://doi.org/10.1017/s0958344010000261

Bazeley KJP (2019) Qualitative data analysis with NVivo. CiNii Books

Belmonte JL, Moreno-Guerrero A-J, Núñez JAL, Lucena FJH (2020) Augmented reality in education. A scientific mapping in web of science. Interact Learn Environ 31(4):1860–1874. https://doi.org/10.1080/10494820.2020.1859546

Botero-Gomez V, Ruiz-Herrera LG, Arias AV, Díaz AR, Garnique JCV (2023) Use of virtual tools in teaching-learning processes: advancements and future direction. Soc Sci 12(2):70. https://doi.org/10.3390/socsci12020070

Briggs N (2018) Neural machine translation tools in the language learning classroom: students’ use, perceptions, and analyses. JALT CALL J 14(1):3–24. https://doi.org/10.29140/jaltcall.v14n1.221

Chen L, Chen P, Lin Z (2020) Artificial intelligence in education: a review. IEEE Access 8:75264–75278. https://doi.org/10.1109/access.2020.2988510

Chen X, Zou D, Cheng G, Xie H (2021) Artificial intelligence-assisted personalized language learning: systematic review and co-citation analysis. In: 2021 international conference on advanced learning technologies (ICALT), pp. 241–245 . IEEE

Chen X, Bear E, Hui B, Santhi-Ponnusamy H, Meurers D (2022) Education theories and AI affordances: Design and implementation of an intelligent computer assisted language learning system. In: Artificial intelligence in education. posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners’ and doctoral consortium, pp 582–585. Springer

Chu H-C, Hwang G-H, Tu Y-F, Yang K-H (2022) Roles and research trends of artificial intelligence in higher education: a systematic review of the top 50 most-cited articles. Australas J Educ Technol 38(3):22–42

Google Scholar  

Cobo MJ, López-Herrera AG, Liu X, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the fuzzy sets theory field. J Inf 5(1):146–166. https://doi.org/10.1016/j.joi.2010.10.002

Cumming G, Sussex R, Cropp S (1993) Learning english as a second language: Towards the “mayday’’ intelligent educational system. Comput Educ. https://doi.org/10.1016/0360-1315(93)90078-w

Daǧdeler KO (2023) A systematic review of mobile-assisted vocabulary learning research. Smart Learn Environ. https://doi.org/10.1186/s40561-023-00235-z

Dizon G (2017) Using intelligent personal assistants for second language learning: a case study of Alexa. TESOL J 8(4):811–830. https://doi.org/10.1002/tesj.353

Dizon G (2021) Affordances and constraints of intelligent personal assistants for second-language learning. RELC J. https://doi.org/10.1177/00336882211020548

Dizon G, Tang D, Yamamoto Y (2022) A case study of using Alexa for out-of-class, self-directed Japanese language learning. Comput Educ Artif Intell 3:100088. https://doi.org/10.1016/j.caeai.2022.100088

Dogan ME, Dogan TG, Bozkurt A (2023) The use of artificial intelligence (AI) in online learning and distance education processes: a systematic review of empirical studies. Appl Sci 13(5):3056. https://doi.org/10.3390/app13053056

Dokukina I, Gumanova J (2020) The rise of chatbots-new personal assistants in foreign language learning. Proc Comput Sci 169:542–546. https://doi.org/10.1016/j.procs.2020.02.212

Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133:285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Esit O (2011) Your verbal zone: an intelligent computer-assisted language learning program in support of Turkish learners’ vocabulary learning. Comput Assist Lang Learn 24(3):211–232. https://doi.org/10.1080/09588221.2010.538702

Fang X, Ng DTK, Leung JKL, Chu S (2023) A systematic review of artificial intelligence technologies used for story writing. Educ Inf Technol. https://doi.org/10.1007/s10639-023-11741-5

Farrokhnia M, Banihashem SK, Noroozi O, Wals AEJ (2023) A SWOT analysis of ChatGPT: implications for educational practice and research. Innov Educ Teach Int. https://doi.org/10.1080/14703297.2023.2195846

Fryer LK, Ainley M, Thompson A, Gibson A, Sherlock Z (2017) Stimulating and sustaining interest in a language course: an experimental comparison of Chatbot and human task partners. Comput Hum Behav 75:461–468. https://doi.org/10.1016/j.chb.2017.05.045

Fryer LK, Nakao K, Thompson A (2019) Chatbot learning partners: connecting learning experiences, interest and competence. Comput Hum Behav 93:279–289. https://doi.org/10.1016/j.chb.2018.12.023

Gayed JM, Carlon MKJ, Oriola AM, Cross JS (2022) Exploring an AI-based writing assistant’s impact on english language learners. Comput Educ Artif Intell 3:100055. https://doi.org/10.1016/j.caeai.2022.100055

Godwin-Jones R (2022) Partnering with AI: intelligent writing assistance and instructed language learning. Lang Learn Technol 26(2):5–24. https://doi.org/10125/73474

Goodell JW, Kumar S, Lim WM, Pattnaik D (2021) Artificial intelligence and machine learning in finance: identifying foundations, themes, and research clusters from bibliometric analysis. J Behav Exp Financ 32:100577. https://doi.org/10.1016/j.jbef.2021.100577

Gruetzemacher R (2022) The power of natural language processing. https://hbr.org/2022/04/the-power-of-natural-language-processing

Gugerty L (2006) Newell and Simon’s logic theorist: historical background and impact on cognitive modeling. Proc Human Fact Ergon Soc Ann Meet 50(9):880–884. https://doi.org/10.1177/154193120605000904

Górriz JM, Ramírez J, Ortíz A, Martinez-Murcia FJ, Segovia F, Suckling J, Leming M, Zhang Y-D, Álvarez-Sánchez JR, Bologna G et al (2020) Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410:237–270

Hariri W (2023) Unlocking the potential of chatgpt: A comprehensive exploration of its applications, advantages, limitations, and future directions in natural language processing. arXiv preprint arXiv:2304.02017

Home. https://hai.stanford.edu/

Hou Y, Yu Z (2023) A bibliometric analysis of synchronous computer-mediated communication in language learning using VOSViewer and CITNetExplorer. Educ Sci 13(2):125. https://doi.org/10.3390/educsci13020125

Huang W, Hew KF, Fryer LK (2021) Chatbots for language learning-Are they really useful? A systematic review of chatbot-supported language learning. J Comput Assist Learn 38(1):237–257. https://doi.org/10.1111/jcal.12610

Huang X, Zou D, Cheng G, Chen X, Xie H (2023) Trends, research issues and applications of artificial intelligence in language education. Educ Technol Soc 26(1):112–131

Jeon JH (2021) Chatbot-assisted dynamic assessment (CA-DA) for L2 vocabulary learning and diagnosis. Comput Assist Lang Learn. https://doi.org/10.1080/09588221.2021.1987272

Jeon JH, Lee S, Choi S (2023) A systematic review of research on speech-recognition chatbots for language learning: implications for future directions in the era of large language models. Interact Learn Environ. https://doi.org/10.1080/10494820.2023.2204343

Ji H, Han I, Ko Y (2022) A systematic review of conversational AI in language education: focusing on the collaboration with human teachers. J Res Technol Educ 55(1):48–63. https://doi.org/10.1080/15391523.2022.2142873

Katsarou E, Wild F, Sougari A-M, Chatzipanagiotou P (2023) A systematic review of voice-based intelligent virtual agents in EFL education. Int J Emerg Technol Learn (IJET) 18(10):65–85. https://doi.org/10.3991/ijet.v18i10.37723

Kessler G (2018) Technology and the future of language teaching. Foreign Lang Ann 51(1):205–218. https://doi.org/10.1111/flan.12318

Kienberger M, García-Holgado A, Schramm K, Raveling A, Meurers D, Labinska B, Koropatnitska T, Therón R (2022) Enhancing adaptive teaching of reading skills using digital technologies: the latill project. In: International conference on technological ecosystems for enhancing multiculturality, pp 1092–1098. Springer

Kohnke L, Moorhouse BL, Zou D (2023) ChatGPT for language teaching and learning. RELC J. https://doi.org/10.1177/00336882231162868

Krippendorff K (2018) Content analysis: an introduction to its methodology. Sage publications, California

Lee S, Jeon JH (2022) Visualizing a disembodied agent: young EFL learners’ perceptions of voice-controlled conversational agents as language partners. Comput Assist Lang Learn. https://doi.org/10.1080/09588221.2022.2067182

Liang J-C, Hwang G-J, Chen M-RA, Darmawansah D (2021) Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach. Interact Learn Environ. https://doi.org/10.1080/10494820.2021.1958348

Lin MP-C, Chang D (2020) Enhancing post-secondary writers’ writing skills with a chatbot. J Educ Technol Soc 23(1):78–92

Litman D, Strik H, Lim GS (2018) Speech technologies and the assessment of second language speaking: approaches, challenges, and opportunities. Lang Assess Q 15(3):294–309. https://doi.org/10.1080/15434303.2018.1472265

Liu C, Liu S, Hwang G-J, Tu Y-F, Wang Y, Wang N (2023) Engaging EFL students’ critical thinking tendency and in-depth reflection in technology-based writing contexts: a peer assessment-incorporated automatic evaluation approach. Educ Inf Technol. https://doi.org/10.1007/s10639-023-11697-6

Liu Z, Wang S, Zhang Y, Feng Y, Li J, Zhu H (2023) Artificial intelligence in food safety: a decade review and bibliometric analysis. Foods 12(6):1242. https://doi.org/10.3390/foods12061242

Luo J (2022) A bibliometric review on artificial intelligence for smart buildings. Sustainability 14(16):10230. https://doi.org/10.3390/su141610230

Ma L (2021) An immersive context teaching method for college english based on artificial intelligence and machine learning in virtual reality technology. Mob Inf Syst 2021:1–7. https://doi.org/10.1155/2021/2637439

Meurers D (2012) Natural language processing and language learning. Encyc Appl Ling 4193–4205. https://www.sfs.uni-tuebingen.de/~dm/papers/meurers-13.pdf

Min W, Yu Z (2023) A bibliometric analysis of augmented reality in language learning. Sustainability 15(9):7235. https://doi.org/10.3390/su15097235

Nagata N (2013) An effective application of natural language processing in second language instruction. CALICO J 13(1):47–67. https://doi.org/10.1558/cj.v13i1.47-67

...Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann T, Mulrow CD, Shamseer L, Tetzlaff J, Akl EA, Brennan S, Chou R, Glanville J, Grimshaw J, Hróbjartsson A, Lalu MM, Li T, Loder E, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart L, Thomas J, Tricco AC, Welch V, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. https://doi.org/10.1136/bmj.n71

Peters ME, Ammar W, Bhagavatula C, Power R (2017) Semi-supervised sequence tagging with bidirectional language models. arXiv preprint arXiv:1705.00108

Pokrivčáková S (2019) Preparing teachers for the application of AI-powered technologies in foreign language education. J Lang Cult Educ 7(3):135–153. https://doi.org/10.2478/jolace-2019-0025

Popenici S, Kerr S (2017) Exploring the impact of artificial intelligence on teaching and learning in higher education. Res Pract Technol Enhanc Learn. https://doi.org/10.1186/s41039-017-0062-8

Rahman A, Tomy P (2023) Intelligent personal assistant–An interlocutor to mollify foreign language speaking anxiety. Interact Learn Environ. https://doi.org/10.1080/10494820.2023.2204324

Rahman A, Karthikeyan J, Tong C (2022) Appropriate Allocation of Specified NLP Tools for the Four Language Skills. Royal Book Publishing-International. https://doi.org/10.1080/10494820.2023.2204324

Ruiz S, Rebuschat P, Meurers D (2019) The effects of working memory and declarative memoryon instructed second language vocabulary learning: insights from intelligent CALL. Lang Teach Res 25(4):510–539. https://doi.org/10.1177/1362168819872859

Rusmiyanto R, Huriati N, Fitriani N, Tyas NK, Rofi’i A, Sari MN (2023) The role of artificial intelligence (AI) in developing english language learner’s communication skills. J Educ 6(1):750–757. https://doi.org/10.31004/joe.v6i1.2990

Russell S, Norvig P (2010) Artificial intelligence: a modern approach, 3rd edn. Prentice Hall, Hoboken

Segler TM, Pain H, Sorace A (2002) Second language vocabulary acquisition and learning strategies in ICALL environments. Comput Assist Lang Learn 15(4):409–422. https://doi.org/10.1076/call.15.4.409.8272

Seising R (2018) The emergence of fuzzy sets in the decade of the perceptron-Lotfi A. Zadeh’s and Frank Rosenblatt’s research work on pattern classification. Mathematics 6(7):110. https://doi.org/10.3390/math6070110

Shadiev R, Liu J (2022) Review of research on applications of speech recognition technology to assist language learning. ReCALL 35(1):74–88. https://doi.org/10.1017/s095834402200012x

Sharadgah TA, Sa’di RA (2022) A systematic review of research on the use of artificial intelligence in english language teaching and learning (2015–2021): what are the current effects? J Inf Technol Educ 21:337–377. https://doi.org/10.28945/4999

Shazly RE (2021) Effects of artificial intelligence on english speaking anxiety and speaking performance: a case study. Expert Syst. https://doi.org/10.1111/exsy.12667

Simonite T (2020) Baidu’s Artificial-Intelligence supercomputer beats Google at image recognition. MIT Technology Review. https://www.technologyreview.com/2015/05/13/168197/baidus-artificial-intelligence-supercomputer-beats-google-at-image-recognition/

Slavuj V, Kovačić B, Jugo I (2015) Intelligent tutoring systems for language learning. In: 38th international convention on information and communication technology, electronics and microelectronics (MIPRO). https://doi.org/10.1109/mipro.2015.7160383

Tai TY, Chen HH-J (2020) The impact of google assistant on adolescent EFL learners’ willingness to communicate. Interact Learn Environ. https://doi.org/10.1080/10494820.2020.1841801

Tan SC, Lee AVY, Lee M (2022) A systematic review of artificial intelligence techniques for collaborative learning over the past two decades. Comput Educ Artif Intell 3:100097. https://doi.org/10.1016/j.caeai.2022.100097

Thayyib PV, Mamilla R, Khan M, Fatima H, Asim M, Anwar I, Shamsudheen MK, Khan MA (2023) State-of-the-Art of artificial intelligence and big data analytics reviews in five different domains: a bibliometric summary. Sustainability 15(5):4026. https://doi.org/10.3390/su15054026

Triantafillou E, Pomportsis AS, Demetriadis S (2003) The design and the formative evaluation of an adaptive educational system based on cognitive styles. Comput Educ 41(1):87–103. https://doi.org/10.1016/s0360-1315(03)00031-9

Turing A (1950) I.-Computing machinery and intelligence. Mind LIX 236:433–460. https://doi.org/10.1093/mind/lix.236.433

Article   MathSciNet   Google Scholar  

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst. 30:5998–6008

Verma S, Gustafsson A (2020) Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach. J Bus Res 118:253–261. https://doi.org/10.1016/j.jbusres.2020.06.057

Weitzman T (2023) GPT-4 released: what it means for the future of your business. Forbes. https://www.forbes.com/sites/forbesbusinesscouncil/2023/03/28/gpt-4-released-what-it-means-for-the-future-of-your-business/?sh=1567d4312dc6

Weng X, Chiu TKF (2023) Instructional design and learning outcomes of intelligent computer assisted language learning: systematic review in the field. Comput Educ Artif Intell 4:100117. https://doi.org/10.1016/j.caeai.2022.100117

Wijekumar K, Meyer BJF, Lei PW (2013) High-fidelity implementation of web-based intelligent tutoring system improves fourth and fifth graders content area reading comprehension. Comput Educ 68:366–379. https://doi.org/10.1016/j.compedu.2013.05.021

Xu Z, Wijekumar K, Ramirez G, Hu X, Irey R (2019) The effectiveness of intelligent tutoring systems on K-12 students’ reading comprehension: a meta-analysis. Br J Edu Technol 50(6):3119–3137. https://doi.org/10.1111/bjet.12758

Yang CT-Y, Lai SL, Chen HH-J (2022) The impact of intelligent personal assistants on learners’ autonomous learning of second language listening and speaking. Interact Learn Environ 1:21. https://doi.org/10.1080/10494820.2022.2141266

Yang H, Kyun S (2022) The current research trend of artificial intelligence in language learning: a systematic empirical literature review from an activity theory perspective. Australasian J Edu Technol 180–210. https://doi.org/10.14742/ajet.7492

Zhang R, Zou D, Cheng G (2023) A review of chatbot-assisted learning: pedagogical approaches, implementations, factors leading to effectiveness, theories, and future directions. Interact Learn Environ. https://doi.org/10.1080/10494820.2023.2202704

Ziegler N, Meurers D, Rebuschat P, Ruiz S, Moreno-Vega JL, Chinkina M, Li W, Grey S (2017) Interdisciplinary research at the intersection of CALL, NLP, and SLA: methodological implications from an input enhancement project. Lang Learn 67(S1):209–231. https://doi.org/10.1111/lang.12227

Zilio L, Wilkens R, Fairon C (2017) Using nlp for enhancing second language acquisition. In: RANLP, pp 839–846

Download references

The study received no external funding.

Author information

Antony Raj, Prajeesh Tomy and Mohamed Sahul Hameed have contributed equally to this work.

Authors and Affiliations

Department of English, School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, 632104, India

Abdur Rahman, Antony Raj, Prajeesh Tomy & Mohamed Sahul Hameed

You can also search for this author in PubMed   Google Scholar

Contributions

AR wrote the main manuscript made substantial contributions to the conception of the work. AR made substantial contributions to the conception, data analysis, and formatting of the work. PT drafted revised it critically for important intellectual content. MSH prepared drafted revised it critically for important intellectual content. All the authors reviewed the manuscript.

Corresponding author

Correspondence to Abdur Rahman .

Ethics declarations

Conflict of interest.

The authors of this paper declare no conflicts of interest related to the collection, analysis, interpretation, or presentation of the findings.

Ethical approval

We have adhered to rigorous ethical standards throughout the research process. We obtained all data from the Scopus database, and we acknowledge Scopus for providing access to this resource. We have complied with the terms and conditions of data usage set forth by Scopus.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Rahman, A., Raj, A., Tomy, P. et al. A comprehensive bibliometric and content analysis of artificial intelligence in language learning: tracing between the years 2017 and 2023. Artif Intell Rev 57 , 107 (2024). https://doi.org/10.1007/s10462-023-10643-9

Download citation

Accepted : 19 December 2023

Published : 01 April 2024

DOI : https://doi.org/10.1007/s10462-023-10643-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Language learning
• Bibliometric analysis
• Natural language processing
• Content analysis
• Find a journal
• Publish with us
• Track your research

Subscribe to the PwC Newsletter

Join the community, edit social preview.

Add a new code entry for this paper

Remove a code repository from this paper, mark the official implementation from paper authors, add a new evaluation result row.

• LANGUAGE MODELLING
• LARGE LANGUAGE MODEL

Remove a task

Add a method

Remove a method, edit datasets, large language model based situational dialogues for second language learning.

29 Mar 2024  ·  Shuyao Xu , Long Qin , Tianyang Chen , Zhenzhou Zha , Bingxue Qiu , Weizhi Wang · Edit social preview

In second language learning, scenario-based conversation practice is important for language learners to achieve fluency in speaking, but students often lack sufficient opportunities to practice their conversational skills with qualified instructors or native speakers. To bridge this gap, we propose situational dialogue models for students to engage in conversational practice. Our situational dialogue models are fine-tuned on large language models (LLMs), with the aim of combining the engaging nature of an open-ended conversation with the focused practice of scenario-based tasks. Leveraging the generalization capabilities of LLMs, we demonstrate that our situational dialogue models perform effectively not only on training topics but also on topics not encountered during training. This offers a promising solution to support a wide range of conversational topics without extensive manual work. Additionally, research in the field of dialogue systems still lacks reliable automatic evaluation metrics, leading to human evaluation as the gold standard (Smith et al., 2022), which is typically expensive. To address the limitations of existing evaluation methods, we present a novel automatic evaluation method that employs fine-tuned LLMs to efficiently and effectively assess the performance of situational dialogue models.

Code Edit Add Remove Mark official

Tasks edit add remove, datasets edit, results from the paper edit add remove, methods edit add remove.

Students learning English at risk as pandemic funding dries up

U .S. school districts are facing a pandemic funding cliff this year that threatens students learning English as a second language.

Why it matters: One in five K-12 students in the U.S. speaks a language other than English at home, and 10% of the student population is enrolled in language development services, according to TNTP, an education research and advocacy organization.

• School closures during COVID-19 had a disproportionate impact on English-language learners, per prior research .

Context: In response to the disruption the pandemic caused to public education, the federal government in 2020 began to dole out billions of dollars for schools through the Elementary and Secondary School Emergency Relief (ESSR) fund .

• Some of those districts used the funding to provide key services to multilingual learners, or students whose first language isn't English.

The latest: The funding is set to expire in September but the government is extending it from 120 days to as long as 14 months.

• Jazmin Flores Peña, a policy analyst at the national nonprofit advocacy group All4Ed, says the extensions will give districts some wiggle room, but the big question right now is what will happen when the funds ultimately run dry.
• "At the end of the day, this becomes about choices" over how much to fund programs for multilingual learners, she says, adding that districts need to have a solid commitment to English learners and seek other funding sources.
• Leticia de la Vara, chief of policy, engagements and external affairs for TNTP, tells Axios Latino the end of pandemic funding is "actually an opportunity to rethink how we will fund for this component of the education budget."

Zoom in: Surry County Schools in rural North Carolina used roughly $539,000 in ESSER funds since 2020 to pay for more staff and new technology for multilingual learners, says LuAnne Llewellyn, the district's director of federal programs.

• About 12% of students in the district's 20 schools are learning English. Although the most common first language is Spanish, there's been a recent increase in Vietnamese speakers, Llewellyn says.
• Using ESSER funds, Surry County Schools hired three new multilingual specialists, narrowing the ratio from one specialist per 95 students to one per 53.
• Those specialists are now able to spend about five days each week with students at various schools.

What they're saying: Llewellyn says it's important to understand that students come to school with varying levels of education and a variety of backgrounds. Some students from Central America, for example, speak Indigenous languages rather than Spanish.

• "There's richness and tapestry woven within all of them because of their culture, their background experiences. So when we work with our students and families, we have to take that into consideration."

What's next: In anticipation of the ESSR funds drying up, the district has already turned to other federal and state sources to hold on to the new staff.

What to watch: The funds helped level the playing field for multilingual learners, who advocates have long said receive fewer resources and funding than other students.

• Some analysts argue the federal government should increase Title III funding allocated to schools with a large share of multilingual learners.
• The Migration Policy Institute urged the federal government to collect and make public information on how states and local districts used ESSER funds to serve multilingual learners.

Subscribe to Axios Latino  to get vital news about Latinos and Latin America, delivered to your inbox on Tuesdays and Thursdays.

Get more international news in your inbox with Axios World.

Help | Advanced Search

Computer Science > Machine Learning

Title: unleashing the potential of large language models for predictive tabular tasks in data science.

Abstract: In the domain of data science, the predictive tasks of classification, regression, and imputation of missing values are commonly encountered challenges associated with tabular data. This research endeavors to apply Large Language Models (LLMs) towards addressing these predictive tasks. Despite their proficiency in comprehending natural language, LLMs fall short in dealing with structured tabular data. This limitation stems from their lacking exposure to the intricacies of tabular data during their foundational training. Our research aims to mitigate this gap by compiling a comprehensive corpus of tables annotated with instructions and executing large-scale training of Llama-2 on this enriched dataset. Furthermore, we investigate the practical application of applying the trained model to zero-shot prediction, few-shot prediction, and in-context learning scenarios. Through extensive experiments, our methodology has shown significant improvements over existing benchmarks. These advancements highlight the efficacy of tailoring LLM training to solve table-related problems in data science, thereby establishing a new benchmark in the utilization of LLMs for enhancing tabular intelligence.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .