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• Published: 24 February 2023

Artificial intelligence in academic writing: a paradigm-shifting technological advance

• Roei Golan   ORCID: orcid.org/0000-0002-7214-3073 1   na1 ,
• Rohit Reddy 2   na1 ,
• Akhil Muthigi 2 &
• Ranjith Ramasamy 2  

Nature Reviews Urology volume  20 ,  pages 327–328 ( 2023 ) Cite this article

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Artificial intelligence (AI) has rapidly become one of the most important and transformative technologies of our time, with applications in virtually every field and industry. Among these applications, academic writing is one of the areas that has experienced perhaps the most rapid development and uptake of AI-based tools and methodologies. We argue that use of AI-based tools for scientific writing should widely be adopted.

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Acknowledgements

The manuscript was edited for grammar and structure using the advanced language model ChatGPT. The authors thank S. Verma for addressing inquiries related to artificial intelligence.

Author information

These authors contributed equally: Roei Golan, Rohit Reddy.

Authors and Affiliations

Department of Clinical Sciences, Florida State University College of Medicine, Tallahassee, FL, USA

Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL, USA

Rohit Reddy, Akhil Muthigi & Ranjith Ramasamy

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Correspondence to Ranjith Ramasamy .

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R.R. is funded by the National Institutes of Health Grant R01 DK130991 and the Clinician Scientist Development Grant from the American Cancer Society. The other authors declare no competing interests.

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Elicit: https://elicit.org/

Penelope.ai: https://www.penelope.ai/

Quillbot: https://quillbot.com/

Semantic Scholar: https://www.semanticscholar.org/

Wordtune by AI21 Labs: https://www.wordtune.com/

Writefull: https://www.writefull.com/

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Golan, R., Reddy, R., Muthigi, A. et al. Artificial intelligence in academic writing: a paradigm-shifting technological advance. Nat Rev Urol 20 , 327–328 (2023). https://doi.org/10.1038/s41585-023-00746-x

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Published : 24 February 2023

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DOI : https://doi.org/10.1038/s41585-023-00746-x

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• Research Guides
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AI-Based Literature Review Tools

• Dialogues: Insightful Facts
• How to Craft Prompts
• Plugins / Extensions for AI-powered Searches
• Cite ChatGPT in APA / MLA
• AI and Plagiarism
• ChatGPT & Higher Education
• Author Profile

Selected AI-Based Literature Review Tools

Updates: See news or release of AI (Beta) across various academic research databases including Web of Science , Scopus , Ebsco , ProQues t, OVID , Dimensions , JStor , Westlaw , and LexisNexis . ********* ********** ********** ********** **********   

Disclaimer: TAMU libraries do not have subscription access to the AI-powered tools listed below the divider line. The guide serves solely as an informational resource. It is recommended that you assess these tools and their usage methodologies independently. ------------------------------------------------------------------------------------------------------------------------------------------------------------- SEMANTIC SCHOLAR

• SCIENTIFIC LITERATURE SEARCH ENGINE - finding semantically similar research papers.
• " A free, AI-powered research tool for scientific literature."  <https://www.semanticscholar.org/>. But login is required in order to use all functions.
• Over 200 millions of papers from all fields of science, the data of which has also served as a wellspring for the development of other AI-driven tools.

The 4000+ results can be sorted by Fields of Study, Date Range, Author, Journals & Conferences

Save the papers in your Library folder. The Research Feeds will recommend similar papers based on the items saved.

Example - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality Total Citations: 22,438   [Note: these numbers were gathered when this guide was created] Highly Influential Citations 2,001 Background Citations 6,109 Methods Citations 3,273  Results Citations 385

TLDRs (Too Long; Didn't Read) Try this example . Press the pen icon to reveal the highlighted key points . TLDRs "are super-short summaries of the main objective and results of a scientific paper generated using expert background knowledge and the latest GPT-3 style NLP techniques. This new feature is available in beta for nearly 60 million papers in computer science, biology, and medicine..." < https://www.semanticscholar.org/product/tldr>

• AI-POWERED RESEARCH ASSISTANT - finding papers, filtering study types, automating research flow, brainstorming, summarizing and more.
• " Elicit is a research assistant using language models like GPT-3 to automate parts of researchers’ workflows. Currently, the main workflow in Elicit is Literature Review. If you ask a question, Elicit will show relevant papers and summaries of key information about those papers in an easy-to-use table."   <https://elicit.org/faq#what-is-elicit.>; Find answers from 175 million papers. FAQS
• Example - How do mental health interventions vary by age group?    /   Fish oil and depression Results: [Login required] (1) Summary of top 4 papers > Paper #1 - #4 with Title, abstract, citations, DOI, and pdf (2) Table view: Abstract / Interventions / Outcomes measured / Number of participants (3) Relevant studies and citations. (4) Click on Search for Paper Information to find - Metadata about Sources ( SJR etc.) >Population ( age etc.) >Intervention ( duration etc.) > Results ( outcome, limitations etc.) and > Methodology (detailed study design etc.) (5) Export as BIB or CSV
• How to Search / Extract Data / List of Concept Search -Enter a research question >Workflow: Searching > Summarizing 8 papers> A summary of 4 top papers > Final answers. Each result will show its citation counts, DOI, and a full-text link to Semantic Scholar website for more information such as background citations, methods citation, related papers and more. - List of Concepts search - e.g. adult learning motivation . The results will present a list the related concepts. - Extract data from a pdf file - Upload a paper and let Elicit extract data for you.
• Export Results - Various ways to export results.
• How to Cite - Includes the elicit.org URL in the citation, for example: Ought; Elicit: The AI Research Assistant; https://elicit.org; accessed xxxx/xx/xx

CONSENSUS.APP

ACADEMIC SEARCH ENGINE- using AI to find insights in research papers.

"We are a search engine that is designed to accept research questions, find relevant answers within research papers, and synthesize the results using the same language model technology." <https://consensus.app/home/blog/maximize-your-consensus-experience-with-these-best-practices/>

• Example - Does the death penalty reduce the crime?   /  Fish oil and depression  /    (1) Extracted & aggregated findings from relevant papers. (2) Results may include AIMS, DESIGN, PARTICIPANTS, FINDINGS or other methodological or report components. (3) Summaries and Full Text
• How to Search Direct questions - Does the death penalty reduce the crime? Relationship between two concepts - Fish oil and depression / Does X cause Y? Open-ended concepts - effects of immigration on local economics Tips and search examples from Consensus' Best Practice   
• Synthesize (beta) / Consensus Meter When the AI recognizes certain types of research questions, this functionality may be activated. It will examine a selection of some studies and provide a summary along with a Consensus Meter illustrating their collective agreement. Try this search: Is white rice linked to diabetes? The Consensus Meter reveals the following outcomes after analyzing 10 papers: 70% indicate a positive association, 20% suggest a possible connection, and 10% indicate no link.

Prompt “ write me a paragraph about the impact of climate change on GDP with citations “

CITATIONS IN CONTEXT

Integrated with Research Solutions.

Over 1.2 billion Citation Statements and metadata from over 181 million papers suggested reference.

How does it work? - "scite uses access to full-text articles and its deep learning model to tell you, for a given publication: - how many times it was cited by others - how it was cited by others by displaying the text where the citation happened from each citing paper - whether each citation offers supporting or contrasting evidence of the cited claims in the publication of interest, or simply mention it."   <https://help.scite.ai/en-us/article/what-is-scite-1widqmr/>

EXAMPLE of seeing all citations and citation statements in one place

More information: Scite: A smart citation index that displays the context of citations and classifies their intent using deep learning  

• GPT3.5 by OpenAI. Knowledge cutoff date is September 2021.
• Input/ Output length - ChatGPT-3.5 allows a maximum token limit of 4096 tokens. According to ChatGPT " On average, a token in English is roughly equivalent to 4 bytes or characters. English words are typically around 5 characters long. This means that, very roughly, you could fit around 800 to 1000 English words within 4096 tokens." 
• According to ChatGPT, the generated responses are non-deterministic by default. So if you run the searches again and get slightly or very different results, it's likely due to this factor.
• ChatGPT may find non-existent references.
• According to this study < https://arxiv.org/ftp/arxiv/papers/2304/2304.06794.pdf > "ChatGPT cites the most-cited articles and journals, relying solely on Google Scholar's citation counts" within the field of environmental science.  
• Example - "INTERVIEW WITH CHATGPT" as a Research Method & Teaching Tool Some researchers began to use this approach to obtain their research data. Try this Google Scholar search link "interview with ChatGPT"   or see two articles below: (1) Chatting about ChatGPT: how may AI and GPT impact academia and libraries? BD Lund, T Wang - Library Hi Tech News, 2023 (2) An interview with ChatGPT: discussing artificial intelligence in teaching, research, and practice , G Scaringi, M Loche - 2023  

Increased risk of sudden infant death syndrome (SIDS) Increased risk of premature birth Increased risk of low birth weight Increased risk of respiratory problems in newborns Increased risk of respiratory problems in infants exposed to secondhand smoke Increased risk of developing asthma and other respiratory illnesses later in life for infants exposed to secondhand smoke [Note : ChatGPT may generate non-existent references or false knowledge. To find out why Large Language Models hallucinate, check out this Wiki article: Hallucination (artificial intelligence) and this blog post -   A Gentle Introduction to Hallucinations in Large Language Models by Adrian Tam ]

Infant death Neonatal mortality (referring specifically to deaths within the first 28 days of life) Perinatal mortality (referring to deaths occurring during the period from 22 completed weeks of gestation to 7 completed days after birth) Early childhood mortality (referring to deaths occurring within the first five years of life) Child mortality (referring to deaths occurring before the age of 18) [Press the Regenerate button to get more.]

• Example -  RELATED WORDS What are the related words of infant mortality? Neonatal mortality, Post-neonatal mortality, Stillbirths, Low birth weight, Malnutrition, Infectious diseases, Vaccination, Maternal health,  Access to healthcare, Poverty, Social inequality, Sanitation, Hygiene, Water quality, Childbirth complications, Congenital abnormalities, Birth defects, Maternal age, Under-five mortality, Child mortality, Perinatal mortality, Preterm birth, Low birth weight, Sudden infant death syndrome (SIDS), Maternal mortality, Postnatal care, Malnutrition, Immunization, Access to healthcare, Clean water and sanitation, Congenital anomalies, Infant health disparities, Infant mortality rate (IMR), Infant survival.  

(("Infant Death"[Mesh] OR "Sudden Infant Death Syndrome"[Mesh] OR "Infant Mortality"[Mesh] OR "Perinatal Mortality"[Mesh] OR "Neonatal Mortality"[Mesh] OR "Fetal Mortality"[Mesh] OR "Infant Death" OR "Sudden Infant Death Syndrome" OR "Infant Mortality" OR "Perinatal Mortality" OR "Neonatal Mortality" OR "Fetal Mortality") AND ("Risk Factors"[Mesh] OR "Etiology"[Mesh] OR "Prevention and Control"[Mesh] OR "Interventions"[Mesh] OR "Public Health"[Mesh] OR "Health Disparities"[Mesh] OR "Healthcare Disparities"[Mesh] OR "Poverty"[Mesh] OR "Malnutrition"[Mesh] OR "Infection"[Mesh] OR "Congenital Abnormalities"[Mesh] OR "Prematurity"[Mesh] OR "Low Birth Weight"[Mesh] OR "Maternal Education"[Mesh] OR "Maternal Health"[Mesh] OR "Risk Factors" OR "Etiology" OR "Prevention and Control" OR "Interventions" OR "Public Health" OR "Health Disparities" OR "Healthcare Disparities" OR "Poverty" OR "Malnutrition" OR "Infection" OR "Congenital Abnormalities" OR "Prematurity" OR "Low Birth Weight" OR "Maternal Education" OR "Maternal Health")) Comments and Suggestions: #1 -This search string may not work on all database platforms. For example, Medline (OVID) may not be able to run it, Medline & CINAHL (EBSCO) can run it without any problems while PubMed will run it but it will indicate which MeSH terms are not indexed. See PubMed's search History and Details under Advanced . #2 - This is just one possible combination. You may need to adjust it based on your task and the databases you are using . #3 - It might be easier if you break the task into 3 parts: ask for synonyms > ask for thesauri terms > and ask it to combine them.

Examples - DATABASE RECOMMENDATIONS Heath Science - Which databases to use to find articles on the EFFECTS OF MATERNAL SMOKING ON INFANT DEATH? PubMed: This is a database of biomedical literature from the National Library of Medicine. It includes articles from medical journals and other sources on a wide range of health-related topics. Embase: This is a database of biomedical literature that covers more international and pharmaceutical content than PubMed. It includes articles from medical journals and other sources on a wide range of health-related topics. Cochrane Library : This is a collection of databases that provides systematic reviews and meta-analyses of healthcare interventions, including interventions related to maternal smoking and infant death. Scopus: This is a multidisciplinary database that covers research from a wide range of fields, including health sciences. It includes articles from scientific journals, books, and conference proceedings. Web of Science: This is a database that covers research from a wide range of fields, including health sciences. It includes articles from scientific journals, books, and conference proceedings. CINAHL: This is a database of nursing and allied health literature, including articles on maternal smoking and infant death. [Note: The response above was copied and pasted directly from ChatGPT. No editing was added.] Education -- I am doing a meta-analysis on k-12 STEM education. What databases should I use? ERIC : This is a database of education research and information, including articles from journals, reports, and conference papers. It is a commonly used database for educational research. Education Source : This is a comprehensive database of education research and information, including articles from journals, books, and conference papers. Web of Science: This is a multidisciplinary database that covers research from a wide range of fields, including education. It includes articles from scientific journals, books, and conference proceedings. Scopus: This is another multidisciplinary database that covers research from a wide range of fields, including education. It includes articles from scientific journals, books, and conference proceedings. PsycINFO: This is a database of psychology research, including articles from journals, books, and dissertations. It can be useful for research on STEM education that includes psychological factors. Cochrane Library : This is a collection of databases that provides systematic reviews and meta-analyses of healthcare interventions including interventions related to education. [Author's note: You could also ask for the GREY LITERATURE sources related to the topic above,]

Example: ACT-AS-AN-EXPERT PROMPT Here's an alternative way to consolidate all the questions into a single consultation by using this popular Act-As-An-Expert prompt, aka, system message prompt:  I am going to conduct a systematic review on games and mathematics related to K-12 education  I want you to act as a systematic review expert . I will ask you some questions related to my research topic and you advise me on how to do it. 

[You may ask one question at a time] My first question is "which databases should I use?"  My second question is "what are the relevant subject terms, descriptors and key words?" My third question is "how to do data extraction?" ..... , .... Please note that the above prompts are merely for illustrative purposes. Actual questions may contain more specific instructions. As an example, for the first question, could you provide guidance on how to identify relevant publications? Specifically, can you provide a list of the primary databases and grey literature sources related to my topic?  

Example - GREY LITERATURE Please show me all the resources that offer grey literature for conducting research in the field of education     ERIC (Education Resources Information Center): https://eric.ed.gov/     OpenGrey: http://www.opengrey.eu/     British Education Index: https://www.ebscohost.com/academic/british-education-index     WorldCat: https://www.worldcat.org/     ProQuest Dissertations & Theses Global: https://www.proquest.com/products-services/pqdtglobal.html     EThOS (Electronic Theses Online Service): https://ethos.bl.uk/     Institutional Repositories of universities and academic institutions (search for specific institution repositories)     American Educational Research Association (AERA): https://www.aera.net/     National Education Association (NEA): https://www.nea.org/     Association for Supervision and Curriculum Development (ASCD): http://www.ascd.org/     RAND Corporation: https://www.rand.org/     National Center for Education Research (NCER): https://ies.ed.gov/ncer/     National Center for Education Statistics (NCES): https://nces.ed.gov/     European Educational Research Association (EERA): https://www.eera-ecer.de/     UNESCO International Institute for Educational Planning (IIEP): http://www.iiep.unesco.org/     Organization for Economic Cooperation and Development (OECD): https://www.oecd.org/education/     Education Development Center (EDC): https://www.edc.org/     National Institute for Educational Policy Research (NIER) - Japan: https://www.nier.go.jp/     Australian Council for Educational Research (ACER): https://www.acer.org/     Canadian Education Association (CEA): https://www.cea-ace.ca/ My topic is about stem education. Which grey literature databases should I use?    STEM Education Research Centre (SERC) Grey Literature Database    National STEM Education Research Repository (NSERR)    National Science Digital Library (NSDL)    STEM Education Resource Center (SERC) GrayLit Network    STEM Education Grey Literature Library

See other Comparison Examples under Bing AI.

Examples - ZERO SHOT, ONE SHOT & FEW SHOT PROMPTING for literature review See the examples on the left panel- How to Fine Tune Prompts

Bing Chat / Copilot

• Bing Chat has become Copilot.
• CONVERSATIONAL LLM 4.0 + SEARCH ENGINE. Connects to the Internet.
• Three conversation modes: More Creative, More Balanced or More Precise.
• Comparison - Prompt: My topic is about psychology. Which grey literature databases should I use? More Precise - PsycExtra, NIH Library and links to library guides, .gov and .org websites More Balanced - PsycExtra and three main research guides that mention psychology grey literature sources. More Creative- PsycExtra, LILACS, and Worldcat + definition of grey literature + suggestions on searching for professional association websites, newsletters, magazines, standards and guidelines, and other sources that may contain relevant information for your topic.
• Some examples below were tested in Bing Chat only.  
• Example - factors of infant mortality Infant mortality is a term that refers to the death of infants before their first birthday. The leading causes of infant mortality include birth asphyxia, pneumonia, congenital malformations, term birth complications such as abnormal presentation of the fetus umbilical cord prolapse, or prolonged labor, neonatal infection, diarrhea, malaria, measles, and malnutrition1. Many situational factors lead to infant mortality such as the pregnant woman’s level of education, environmental conditions, political infrastructure, and level of medical support1. In the United States, a primary determinant of infant mortality risk is infant birth weight with lower birth weights increasing the risk of infant mortality. The determinants of low birth weight include socioeconomic, psychological, behavioral and environmental factors2.  
• Example: PEER REVIEWED SOURCES I need to find some peer reviewed articles about infant mortality? The answer includes 5 articles. The first one is “Causes of death and infant mortality rates among full-term births in the United States between 2010 and 2012: An observational study” by Neha Bairoliya and Günther Fink1. A click on  " Tell me more about the first article " leads to this summary: " The first article is titled “Causes of death and infant mortality rates among full-term births in the United States between 2010 and 2012: An observational study” and was published in PLOS Medicine on March 20, 2018. The authors are Neha Bairoliya and Günther Fink. The study used linked birth and death records for the period 2010–2012 to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37–42 weeks of gestation) in the United States. The researchers found that the full-term infant mortality rate (FTIMR) was 2.2 per 1,000 live births overall and ranged between 1.29 (Connecticut) and 3.77 (Mississippi) at the state level. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) and suffocation." More searches: Please find five empirical studies focusing on the factors influencing adult learners engaged in online education. Please locate five scholarly articles on stress management among college students site:pubmed.ncbi.nlm.nih.gov

The ERIC Thesaurus contains nearly 12,000 terms, including more than 4,500 descriptors and 7,000 synonyms1. You can use the term “Academic Achievement” or its synonyms such as “Educational Achievement”, “Student Achievement”, “Academic Performance”, “Academic Success”, “Learning Achievement” and more. [ Note : The follow-up responses include some additional sources. See two below: https://www.powerthesaurus.org/academic_achievement https://eric.ed.gov/?ti=Achievement+Gap   including https://eric.ed.gov/?ti=Academic+Achievement |

My topic is about psychology.  Which grey literature databases should I use?

ChatGPT PsycEXTRA : This database is produced by the American Psychological Association (APA) and includes grey literature such as conference papers, white papers, and government reports. OpenGrey : This database provides access to grey literature produced in Europe, including reports, theses, and conference papers Grey Literature Report : This database is produced by The New York Academy of Medicine and includes reports, conference papers, and other grey literature on health and biomedical topics, including psychology. ProQuest Dissertations and Theses Global : This database provides access to graduate-level research, including theses and dissertations related to psychology. Google Scholar: While not exclusively a grey literature database, Google Scholar can be a useful tool for finding conference papers and other grey literature related to psychology. National Technical Information Service (NTIS) : This database provides access to technical reports produced by U.S. government agencies, including those related to psychology. Bing AI   - [asked in the Precise mode] For psychology, some recommended grey literature databases include PsycEXTRA and PsyArXiv . PsycEXTRA is a database of grey literature material relating to psychology, behavioral sciences and health. PsyArXiv is a psychology archive designed to facilitate rapid dissemination of psychological research. What other models have been developed based on SERVQUAL?

ChatGPT SERVPERF: This is a modified version of SERVQUAL that measures service performance instead of service quality. It was developed by Cronin and Taylor (1992) and is based on the same five dimensions as SERVQUAL. SERVPERF-GAP: This model, developed by Caruana and Ewing (2006), extends the SERVQUAL model to include three additional gaps that can occur when customers evaluate service quality. E-SERVQUAL: This model, developed by Parasuraman et al. (2005), adapts the SERVQUAL scale to measure the quality of online services. SERVCO: This is a modified version of SERVQUAL that includes a sixth dimension, "cost," in addition to the five original dimensions. It was developed by Sureshchandar et al. (2002) to address the importance of cost in service quality evaluation. SERVQ-HC: This is a modified version of SERVQUAL that is specifically designed for evaluating the quality of health care services. It was developed by Cronin et al. (2000) and includes 20 items across five dimensions. Bing AI The Nordic model of service quality2. The hierarchical model of service quality3. The American Customer Satisfaction Index (ACSI)4.

     Bing AI Sidebar - CHAT, THINK, & WRITE

• Examples of Using AI sidebar to Recap Google Scholar findings

Chat - If you highlight a study in the search results of Google Scholar, you will be prompted with a question by AI sidebar asking if you want to send the selected text to it. If you choose to do so, you can then select either  EXPLAIN or EXPAND to learn more about the chosen article. It's worth noting that EXPAND provides more detailed information. You can then click on any of the follow-up links to delve further into the selected article or topic. If the text of the chosen study is written in a foreign language, the AI sidebar can also provide an explanation of the text in English. Another option for extracting the key points from a selected article is to submit the URL of the PDF. Insights - Use the insights button to get more ideas about the topic. Compose - Tell the Edge sidebar your topic and let its Compose tool to generate a draft for you. Google Scholar Button- See also the Plugins and Extension page for how to perform a combined search using Google, Google Scholar, and ChatGPT .

Example: FINDING SIMILAR ARTICLES Find similar articles like this one - [then paste in the abstract ] Note - Use this prompt with Bing chat since real-time web info is required.

Examples: SUMMARIZE + REFERENCE Use a basic prompt like " Please summarize . ..." or add a REFERENCE TEXT that provides the necessary information about what you want it to focus or how the summary or resolution should look like. The reference could be a quote, an excerpt, a partial sentence, a conversation, an abstract, a code snippet and so on. Formula : Specify a role > assign a task > provide a reference text. Example#1 - Scholarly Journal Quote

STEP 1 Prompt: As a research assistant specializing in renewable energy, your task is to locate comparable research studies based on the provided abstract from an article: "This paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and replacement of fossil fuels by various sources of renewable energy."

Bing Chat then found 3 documents: the ScienceDirect paper where the abstract came from and two other documents from un.org and frontiersin.org respectively.

STEP 2 Prompt: Please synthesize the findings of the three studies provided above.

Bing Chat: "Sure! Here is a synthesis of the findings from the three studies:

All three studies emphasize the importance of renewable energy in achieving sustainable development and mitigating climate change. They all discuss various strategies and actions that can be taken to increase the use of renewable energy sources and reduce reliance on fossil fuels.

Henrik Lund’s paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and .....

The United Nations outlines five critical actions the world needs to prioritize now to transform our energy systems and speed up the shift to renewable energy: Make renewable energy technology a global public good, Improve global access to components and raw materials......

“Sustainable Energy Transition for Renewable and Low Carbon Grid” discusses that a sustainable global electricity transition will entail increased use of renewable energy sources particularly wind and solar, nuclear energy as a low carbon energy source, electrification of transport and.......

In summary , these studies suggest that transitioning to renewable energy sources is crucial for sustainable development and mitigating climate change. This can be achieved through various strategies such as increasing energy efficiency, improving access to renewable technology and materials, leveling the playing field for renewables, ending fossil fuel subsidies, and redirecting investments towards renewables." Example #2 Y ou are an expert researcher, given the following article, please discuss its key findings. Reference: How Large Language Models are Transforming Machine-Paraphrased Plagiarism at https://arxiv.org/abs/2210.03568 This is another way to put REFERENCE TEXT. As a side not, role specifying or the use of persona is important as it can impact the level of responses obtained. Just imagine if the designated role were " You are a high school student... ." Example #3 Please read this article and summarize it f or me - "Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making" and then find 5 similar studies and please also summarize each for me.

CONNECTED PAPERS

• RELATED STUDIES
• Uses visual graphs or other ways to show relevant studies. The database is connected to the Semantic Scholar Paper Corpus which has compiled hundreds of millions of published papers across many science and social science fields.
• See more details about how it works .  
• Example - SERVQUAL and then click on SELECT A PAPER TO BUILD THE GRAPH > The first paper was selected. Results: (1) Origin paper - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality + Connected papers with links to Connected Papers / PDF / DOI or Publisher's site / Semantic Scholar / Google Scholar. (2) Graph showing the origin paper + connected papers with links to the major sources . See above. (3) Links to Prior Works and Derivative Works See the detailed citations by Semantic Scholar on the origin SERVQUAL paper on the top of this page within Semantic Scholars.
• How to Search Search by work title. Enter some keywords about a topic.
• Download / Save Download your saved Items in Bib format.

PAPER DIGEST

• SUMMARY & SYNTHESIS
• " Knowledge graph & natural language processing platform tailored for technology domain . <"https://www.paperdigest.org/> Areas covered: technology, biology/health, all sciences areas, business, humanities/ social sciences, patents and grants ...

• LITERATURE REVIEW - https://www.paperdigest.org/review/ Systematic Review - https://www.paperdigest.org/literature-review/
• SEARCH CONSOLE - https://www.paperdigest.org/search/ Conference Digest - NIPS conference papers ... Tech AI Tools: Literature Review  | Literature Search | Question Answering | Text Summarization Expert AI Tools: Org AI | Expert search | Executive Search, Reviewer Search, Patent Lawyer Search...

Daily paper digest / Conference papers digest / Best paper digest / Topic tracking. In Account enter the subject areas interested. Daily Digest will upload studies based on your interests.

RESEARCH RABBIT

• CITATION-BASED MAPPING: SIMILAR / EARLY / LATER WORKS
• " 100s of millions of academic articles and covers more than 90%+ of materials that can be found in major databases used by academic institutions (such as Scopus, Web of Science, and others) ." See its FAQs page. Search algorithms were borrowed from NIH and Semantic Scholar.

The default “Untitled Collection” will collect your search histories, based on which Research Rabbit will send you recommendations for three types of related results: Similar Works / Earlier Works / Later Works, viewable in graph such as Network, Timeline, First Authors etc.

Zotero integration: importing and exporting between these two apps.

• Example - SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality [Login required] Try it to see its Similar Works, Earlier Works and Later Works or other documents.
• Export Results - Findings can be exported in BibTxt, RIS or CSV format.

CITING GENERATIVE AI

• How to cite ChatGPT  [APA] - https://apastyle. apa.org/blog /how-to-cite-chatgpt  
• How to Cite Generative AI  [MLA]  https://style. mla.org /citing-generative-ai/
• Citation Guide - Citing ChatGPT and Other Generative AI (University of Queensland, Australia)
• Next: Dialogues: Insightful Facts >>
• Last Updated: Apr 16, 2024 5:12 PM
• URL: https://tamu.libguides.com/c.php?g=1289555

Systematic and Literature Reviews

• Literature Reviews - overview
• Types of review
• What's involved?
• PRISMA 2020
• Systematic Reviews - Planning
• Systematic Reviews - Where to Search
• Systematic Reviews - Screening and Assessing Records
• Organising your articles
• Endnote / Reference Management
• Systematic Reviews - Resources
• Scoping Reviews
• Using AI's in reviews
• Library Support
• Systematic Review Reading List

Can Artificial Intelligence (AI) tools such as ChatGPT be used to produce systematic reviews?

ChatGPT can certainly produce a convincing looking review, but there are a few issues:

• This includes referencing. ChatGPT will produce convincing looking references that do not refer to an actual source.
• it may also mean that answers do not include recent evidence, depending on when the corpus of literature ChatGPT was trained on dates to, and how often it is retrained or updated. Which we don't know
• For general enquiries these tools may produce 'good enough' answers, but for systematic reviews there is an expectation of transparency of method, rigour of assessment and so forth. These are absent from ChatGPT answers - we don't what it searched or how, or how it selected the references it chose to use in its answer. 

So this all sounds terrible - as quick and plausible as ChatGPT may be, the response it produces may include false information, false sources and a completely opaque methodology. So is there any way it can be used?

• this does not include writing search strategies. ChatGPT can produce a convincing search strategy, but - surprise! - it has been shown to make up components such as MeSH terms that don't exist. While it can handle Boolean operators easily it seems to (so far) not make use of functions such as truncation, wildcard characters or proximity searches.
• Elicit.org . Elicit does not generate answers, rather it uses the same Large Language Model (LLM) training as ChatgPT to interpret your question. It then searches  the 115M papers from the Semantic Scholar Academic Graph database and shows ranked snippets from the best results. It is best used to find research (especially on difficult to search topics) and generate ideas, than to produce any form of ready to go answer. It can  be prompted to extract aspects of interest from the results such as population, outcomes measured or main findings.
• Perplexity.ai  Perplexity combines AI with web search to produce ready made answers. It cites its sources, which are real but tend not to be scholarly. Again it is possibly best suited to generating ideas and identifying sources than to any significant contribution to producing a review.

Of course all of this will change. The use of AI for evidence synthesis is a rapidly developing field, but for clinical use it will still be necessary that syntheses meet the underlying standards of transparency and rigour which are so far absent. Keep this in mind when reading the latest tech hype.

Further Reading

Guidance for Authors, Peer Reviewers, and Editors on Use of AI, Language Models, and Chatbots - JAMA July 2023

Systematic Reviewing and ChatGPT - PICO Portal webinar

What academic research is ChatGPT accessing?  LinkedIn post

How Q&A systems based on large language models (eg GPT4) will change things if they become the dominant search paradigm - 9 implications for libraries.  Blog

Using Large language models like GPT to do Q&A over papers (II) — using Perplexity.ai (free) over CORE, Scite.ai, Semantic Scholar etc domain s. Blog

Academic Publishers Are Missing the Point on ChatGPT . Blog - Scholarly Kitchen.

Using artificial intelligence methods for systematic review in health sciences: A systematic review.  Res Syn Meth . 2022; 13( 3): 353- 362. doi: 10.1002/jrsm.1553

• << Previous: Scoping Reviews
• Next: Library Support >>
• Last Updated: Feb 9, 2024 1:35 PM
• URL: https://libguides.mh.org.au/systematic_and_literature_reviews

Accelerate your dissertation literature review with AI

Become a lateral pioneer.

Get started for free and help craft the future of research.

Early access. No credit card required.

Introduction

Dissertation writing is part of being a graduate student. There are many different ways to organise your research, and several steps to this process . Typically, the literature review is an early chapter in the dissertation, providing an overview of the field of study. It should summarise relevant research papers and other materials in your field, with specific references. To understand how to write a good literature review, we must first understand its purpose. The goals of a literature review are to place your dissertation topic in the context of existing work (this also allows you to acknowledge prior contributions, and avoid accusations of plagiarism), and to set you up to show you are making a new contribution to the field. Since literature review is repetitive, many students find it tedious. While there are some traditional tools and techniques to help, covered below, they tend to be cumbersome and keyword-based. For this reason, we built a better tool for research and literature review, which I describe in the last section. You can see the Lateral tool in action , and how it makes the literature review a lot easier. To sign up to the tool, click here.

1. Different kinds of reading

We can divide the activity of reading for research into three different kinds: 

• Exploratory reading, mostly done in the initial phase;
• Deep reading of highly informative sources; and 
• Broad, targeted skim reading of large collections of books and articles, in order to find specific kinds of information you already know exist.

1.1. Exploratory reading

Initially, a research student will need to read widely in a new field to gain fundamental understanding. In this early stage, the goal is to explore and digest the main ideas in existing research. Traditionally, this phase has been a manual process, but there is a new generation of digital tools to aid in getting a quick overview of your field, and more generally to organise your research . This stage can happen both before and after the research topic or question has been formulated. It is often unstructured and full of serendipitous (“happy accidental”) discovery  — the student’s job is to absorb what they find, rather than to conduct a targeted search for particular information. ‍

Put another way: You don’t know what you’re looking for ahead of time. By the end of this phase, you should be able to sketch a rough map of your field of study.

1.2. Narrow, deep reading

After the exploratory reading phase, you will be able to prioritise the information you read. Now comes the second phase: Deep, reflective reading. In this phase, your focus will narrow to a small number of highly relevant sources — perhaps one or two books, or a handful of articles — which you will read carefully, with the goal of fully understanding important concepts. This is a deliberative style of reading, often accompanied by reflective pauses and significant note taking. If the goal in the first phase was sketching a map of the globe, the goal in this second phase is to decide which cities interest you most, and map them out in colour and detail.

1.3. Broad, targeted reading

You have now sketched a map of your field of study (exploratory reading), and filled in some parts of this map in more detail (narrow, deep reading). I will assume that by this point, you have found a thesis question or research topic, either on your own, or with the help of an advisor. This is often where the literature review begins in earnest. In order to coherently summarise the state of your field, you must review the literature once again, but this time in a more targeted way: You are searching for particular pieces of information that either illustrate existing work, or demonstrate a need for the new approach you will take in your dissertation. For example, 

• You want to find all “methodology” sections in a group of academic articles, and filter for those that have certain key concepts;
• You want to find all paragraphs that discuss product-market fit, inside a group of academic articles.

To return to the map analogy: This is like sketching in the important roads between your favourite cities — you are showing connections between the most important concepts in your field, through targeted information search.

2. Drawbacks of broad targeted reading

The third phase — broad, targeted reading, where you know what kind of information you’re looking for and simply wish to scan a collection of articles or books to find it — is often the most mechanical and time consuming one. Since human brains tend to lose focus in the face of dull repetition, this is also a tedious and error-prone phase for many people. What if you miss something important because you’re on autopilot? Often, students end up speed- or skim reading through large volumes of information to complete the literature review as quickly as possible. With focus and training, this manual approach can be efficient and effective, but it can also mean reduced attention to detail and missed opportunities to discover relevant information. Only half paying attention during this phase can also lead to accidental plagiarism, otherwise known as cryptomnesia: Your brain subconsciously stores a distinctive idea or quote from the existing literature without consciously attributing it to its source reference. Afterwards, you end up falsely, but sincerely believing you created the idea independently, exposing yourself to plagiarism accusations.

3. Existing solutions to speed up literature reviews

Given the drawbacks of manual speed- or skim-reading in the broad reading phase, it’s natural to turn to computer-driven solutions. One popular option is to systematically create a list of search term keywords or key phrases, which can then be combined using boolean operators to broaden results. For example, in researching a study about teenage obesity, one might use the query:

• “BMI” or “obesity” and “adolescents” and not “geriatric”,

to filter for obesity-related articles that do mention adolescents, but don’t mention older adults.

Constructing such lists can help surface many relevant articles, but there are some disadvantages to this strategy:

• These keyword queries are themselves fiddly and time-consuming to create.
• Often what you want to find is whole “chunks” of text — paragraphs or sections, for example — not just keywords.
• Even once you have finished creating your boolean keyword query list, how do you know you haven’t forgotten to include an important search query?

This last point reflects the fact that keyword searching is “fragile” and error-prone: You can miss results that would be relevant — this is known as getting “false negatives” — because your query uses words that are similar, but not identical to words appearing in one or more articles in the library database. For example, the query “sporting excellence” would not match with an article that mentioned only “high performance athletics”.

4. Lateral — a new solution

To make the process of finding specific information in big collections of documents quicker and easier — for example, in a literature review — search, we created the Lateral app , a new kind of AI-driven interface to help you organise, search through and save supporting quotes and information from collections of articles. Using techniques from natural language processing, it understands, out-of-the-box, not only that “sporting excellence” and “high-performance” athletics are very similar phrases, but also that two paragraphs discussing these topics in slightly different language are likely related. Moreover, it also learns to find specific blocks of information, given only a few examples. Want to find all “methodology” sections in a group of articles? Check. How about all paragraphs that mention pharmaceutical applications? We have you covered. If you’re interested, you can sign up today .

5. Final note — novel research alongside the literature review

Some students, to be more efficient, use the literature review process to collect data not just to summarise existing work, but also to support one or more novel theses contained in their research topic. After all, you are reading the literature anyway, so why not take the opportunity to note, for example, relevant facts, quotes and supporting evidence for your thesis? Because Lateral is designed to learn from whatever kind of information you’re seeking, this process also fits naturally into the software’s workflow.

References:

• Is your brain asleep on the job?: https://www.psychologytoday.com/us/blog/prime-your-gray-cells/201107/is-your-brain-asleep-the-job
• Tim Feriss speed reading: https://www.youtube.com/watch?v=ZwEquW_Yij0
• Five biggest reading mistakes: https://www.timeshighereducation.com/blog/five-biggest-reading-mistakes-and-how-avoid-them
• Skim reading can be bad: https://www.inc.com/jeff-steen/why-summaries-skim-reading-might-be-hurting-your-bottom-line.html
• Cryptomnesia: https://en.wikipedia.org/wiki/Cryptomnesia
• Systematic literature review with boolean keywords: https://libguides.library.cqu.edu.au/c.php?g=842872&p=6024187

Lit review youtube intro: https://www.youtube.com/watch?v=bNIG4qLuhJA

Spread the word

There is a better way than Dropbox and Google Drive to do collaborative research

In this blog, I describe the limitations of Dropbox and Google in the space of research, and propose Lateral as the much needed alternative.

Remote group work and the best student collaboration tools

In this blog, I outline some organisational techniques and the best digital collaborative tools for successful student group work.

6 things to consider and organise before writing your dissertation (and how Lateral can help)

I hope the following six things to consider and organise will make the complex dissertation writing more manageable.

Get into flow.

The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

The influence of AI in scientific and academic research is an exciting development, opening the doors to more efficient, comprehensive, and rigorous exploration.

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my Youtube channel.

Best ChatGPT interface – Chat with PDFs/websites and more

I get more out of ChatGPT with HeyGPT . It can do things that ChatGPT cannot which makes it really valuable for researchers.

Use your own OpenAI API key ( h e re ). No login required. Access ChatGPT anytime, including peak periods. Faster response time. Unlock advanced functionalities with HeyGPT Ultra for a one-time lifetime subscription

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Connected Papers –  https://www.connectedpapers.com/
• Research rabbit – https://www.researchrabbit.ai/
• Laser AI –  https://laser.ai/
• Litmaps –  https://www.litmaps.com
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Bit AI –  https://bit.ai/
• Consensus –  https://consensus.app/
• Exper AI –  https://www.experai.com/
• Hey Science (in development) –  https://www.heyscience.ai/
• Iris AI –  https://iris.ai/
• PapersGPT (currently in development) –  https://jessezhang.org/llmdemo
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Open Read –  https://www.openread.academy
• Chat PDF – https://www.chatpdf.com
• Explain Paper – https://www.explainpaper.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Paper Wizard –  https://paperwizard.ai/
• Jenny.AI https://jenni.ai/ (20% off with code ANDY20)
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• Paper Pal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!

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The Best 8 AI-Powered Tools for Literature Review

In this blog post, I will explore the benefits of using best AI-powered tools for literature review . Literature survey is a crucial step in any research work, but it can also be a daunting and time-consuming task.

You have to find, read, and evaluate hundreds or thousands of research papers, synthesize the existing knowledge, and identify the gaps and opportunities for your research.

Fortunately, there are some AI-powered apps that can help you with literature survey and make it easier and faster. In order to automate the tasks such as searching for articles, identifying relevant papers, and summarizing papers the apps use the artificial intelligence.

Besides, this saves you a significant amount of time and effort. It lets you focus on the more creative and strategic aspects of your research work.

Here are some of the best 8 AI-powered apps for literature review that you should try:

Research Rabbit

Research Rabbit is an AI-powered app that helps researchers find, organize, and analyze research papers.

It is a free tool that can be used by anyone, regardless of their research experience.

Research Rabbit uses AI to scan the web for relevant scholarly articles. In addition to that the AI-based tool allows you to save and organize articles in your own personal library.

To use Research Rabbit, you first need to create an account. Once you have created an account, you can start searching for research papers. You can search by keyword, topic, or author.

Once you have found the papers you are interested in, you can add them to your collections. Collections are a way to organize your research papers. You can create as many collections as you need.

Research Rabbit- Link

Rayyan is an AI-powered app that helps researchers conduct systematic literature review s.

A systematic literature review is a comprehensive research process that involves identifying, evaluating, and synthesizing all the available research on a particular topic.

It helps you work quickly through even your largest reviews. It lets you de-duplicate, screen, and organize references, as well as collaborate with your team and generate reports.

You can use Rayyan to import references from various sources, apply inclusion and exclusion criteria, assign labels and ratings, and export your data for further analysis.

Rayyan- Link

Scholarcy is an AI-powered app that can help you with your academic reading. It can automatically summarize articles, create flashcards, and generate bibliographies.

Scholarcy can also help you find related research and identify key information in articles.

In order to use Scholarcy, you first need to create an account. Once you have created an account, you can start adding articles to your library.

Additionally, you can add academic papers from a variety of sources like Google Scholar, PubMed, and your personal library.

Once you have added scholarly articles to your library, you can start using Scholarcy’s features. To summarize an article, simply click on the “Summarize” button.

Scholarcy will then generate a summary of the article in a few seconds.

Overall, Scholarcy is a valuable tool that can help you with your academic reading. If you are looking for a way to save time, improve your understanding, and increase your productivity, Scholarcy is a great option.

Scholarcy- Link

Lateral is an AI-powered app that can help you with your academic research. It can help you find relevant research, organize your findings, and write better papers.

Lateral uses artificial intelligence to analyze research papers and identify key concepts, relationships, and trends. This information can then be used to help you find relevant research, organize your findings, and write better papers.

It helps you organize, search, and save information from collections of articles. You can import articles from various sources, create tags and notes, and use natural language queries to find specific information in your collections.

You can use Lateral to manage your literature review workflow , keep track of your sources and citation s, and access your information anytime and anywhere.

Lateral – Link

Scite is a free, open-source AI-powered app that helps researchers better discover and understand research articles. Scite does this by providing Smart Citation s, which display the context of a citation and describe whether the article provides supporting or contrasting evidence.

To use Scite, simply go to the Scite website and sign up for an account. Once you have an account, you can start searching for research articles. Scite will display a list of articles that match your search criteria, and you can then click on an article to view its Smart Citation s.

You can use Scite to assess the quality and reliability of the literature, avoid citing unreliable sources, and find evidence for or against any hypothesis.

Scite- Link

If you are looking for a way to quickly and easily find answers from scientific research, you might want to try Consensus AI, a new search engine that uses artificial intelligence to extract and distill findings directly from peer-reviewed studies.

Consensus AI is a powerful tool that can help you find evidence-based answers from scientific research in a fast and easy way.

You can also use various features to refine your search and explore more options. Consensus AI can help you save time and effort in finding reliable information from scientific research.

Consensus- Link

Semantic Scholar

It is a free AI-powered academic search engine that helps you find relevant academic papers faster and easier than traditional methods. It uses natural language processing and machine learning to understand your research needs and provide you with smart filters, citation analysis, and key insights from papers.

You can use Semantic Scholar to discover the most influential and recent scientific literature in your field, compare different methods and results, and track the impact of your own publications.

Semantic Scholar- Link

It is an AI-powered reading assistant that helps you organize, annotate, and collaborate on your research. It helps you fine-tune your literature search, generate summaries, highlight and compare ideas, and share and discuss literature with your team.

You can use RAx to create different projects for your research topics, import articles from various sources, annotate them with notes and comments, and get personalized suggestions for further reading.

RAx Literature Review and Critical Analysis Tool-  Link

It is an AI-powered research assistant that can help you with tasks such as finding relevant articles, summarizing articles, and generating research ideas.

A comprehensive platform for all your research processing: Smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction and systematizing of data

Iris AI -Your Research Workspace- Link

These are some of the best 8 AI-powered apps for literature review that can help you save time and effort, improve your understanding and writing skills, and produce high-quality research. Try them out today and see how they can supercharge your literature review process.

It is important to note that AI-powered apps are not a replacement for hum an judgment. However, they can be a valuable tool for helping you to find relevant research articles, identify important concepts, and track the development of research over time.

If you are interested in trying one of these apps, I encourage you to visit their websites and sign up for a free trial. You can also read reviews from other users to get a better idea of which app is right for you.

If you want to supercharge your literature review with AI, you should try out these 8 apps today. They are easy to use, affordable, and reliable. They will transform your literature review process and help you achieve your research goals faster and easier.

Don’t wait any longer. Start using these AI-powered apps for literature review today and see the difference for yourself. You will be amazed by how much they can help you with your research.

I hope this blog post has been helpful. If you have any questions, please feel free to leave a comment below.

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Using scite for literature reviews and critical analysis

Using scite to speed up literature reviews and critical analysis 🚀, finding relevant works, scite search: sorting, citation filters and aggregations.

Collecting works in a dashboard and staying up to date on new citations and publications

Citation chaining, forward chaining.

Backward chaining

Side chaining (finding co-citations)

Critically Engaging with a publication and its results

Using results, arguments, methodologies presented citation statements to perform a critical analysis.

Citation statement search

Adding nuance to your citations

Identifying gaps in the literature, understanding the current state of the art.

Finding differences in results

Finding suggested areas of investigation

Updated on: 09/03/2021

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Millions of researchers are already using SciSpace on research papers. Join them and start using your AI research assistant wherever you're reading online.

Mushtaq Bilal, PhD

Researcher @ Syddansk Universitet

SciSpace is an incredible (AI-powered) tool to help you understand research papers better. It can explain and elaborate most academic texts in simple words.

Olesia Nikulina

PhD Candidate

Academic research gets easier day by day. All thanks to AI tools like @scispace Copilot, Copilot can instantly answer your questions and simply explain scientific concepts as you read

Richard Gao

Co-founder evoke-app.com

This is perfect for a layman to scientific information like me. Especially with so much misinformation floating around nowadays, this is great for understanding studies or research others may have misrepresented on purpose or by accident.

Product Hunt

I absolutely adore this product. It's been years since I was in a lab but, I plugged in a study I did way back when and this gets everything right. Equations, hypotheses, and methodologies will be game changers for graduate studies (the current education system severely limits comprehension while encouraging interconnectivity between streams). But, early learners would be able to discover so many papers through this as well. In short, love it

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I'm gonna recommend SciSpace to all of my friends and family that are still studying. And I'll definitely love to give it a try for myself, cause you know, education should never stop when you finish your studies. 😀

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Wonderful idea! I know this will be used and appreciated by a lot of students, researchers, and lovers of knowledge. Great job, team @saikiranchandha and @shanukumr!

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SVNIT'25 Chemical Engineering

SciSpace, is a website where you can easily conduct research. Its most notable feature, in my opinion, is the presence of a #ai-powered copilot which can #simplify and explain any text you highlight in the paper you're reading. #citations and related papers are easily accessible with each paper.

Researcher @ VIU

It´s not only the saved time. Reading scientific literature, specially if you are not an expert in the field is a very attention-intensive process. It´s not a task you can maintain for long periods of time. Having them not just smartly summarised but being able to get meaningful answers is a game-changer for a science journalist

Kalyani Korla, PhD

Product Manager • Healthcare

Upload your pdf and highlight the sections you want to understand. It simplifies those complicated sections of the article in a jiffy. It is not rocket science, but it is always welcome if someone explains the big terms in simpler words.

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Revolutionize Your Research with Jenni AI

Literature Review Generator

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Designed for ease of use, making it accessible for users with varying levels of tech proficiency.

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Overreliance on AI: Literature Review

• Samir Passi ,
• Mihaela Vorvoreanu

MSR-TR-2022-12 | June 2022

Published by Microsoft

Overreliance on AI occurs when users start accepting incorrect AI outputs. This can lead to issues and errors that can ultimately make people lose trust in AI systems. This report explains what overreliance on AI is, how it happens, and how we can mitigate it. An important goal of AI system design is to empower users to develop appropriate reliance on AI. This is important given that policymakers and practitioners call for greater human oversight—making users the last line of defense against AI failures. This report shows how and why overreliance on AI makes it difficult for users to meaningfully leverage the strengths of AI systems and to oversee their weaknesses. Based on a literature review of ~60 papers from different research areas, this report provides a detailed overview of how overreliance on AI happens, how to measure overreliance, what its consequences are, and how we can minimize its negative effects.

See also our 2024 research synthesis about Appropriate reliance on Generative AI

Cite as: Samir Passi & Mihaela Vorvoreanu. 2022.  Overreliance on AI: Literature Review. Microsoft Technical Report MSR-TR-2022-12. Microsoft Corporation.

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April 18, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

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Researchers perform critical literature review on fairness and AI in the labor market

by Leiden University

Carlotta Rigotti and Eduard Fosch-Villaronga have published a new article that offers an insightful and critical literature review on fairness and AI in the labor market as part of the BIAS project.

The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination.

Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups . However, the ideals and ambitions of fairness may signify different meanings to various stakeholders.

To fill this gap, Rigotti and Fosch-Villaronga worked on a European endeavor titled "Fairness, AI & Recruitment" published in the journal Computer Law & Security Review . This piece, part of the HE BIAS project, provides a critical literature review on the intersection of fairness and AI in the labor market .

Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market.

Scoping literature review

To that end, Carlotta and Eduard conducted a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation.

They started by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. Then they moved to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination.

Against this backdrop, Carlotta and Eduard focused on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, they observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge.

They conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.

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Forget the Humane AI Pin — this new AI pendant from Limitless actually looks useful

Never forget what was said in a meeting or even at the office ever again

From the recently launched Humane AI Pin to the upcoming rabbit r1 , AI-powered gadgets and wearables seem to be all the rage right now. However, while the Humane AI Pin stumbled out of the gate and the jury is still out on the rabbit r1, a new contender is entering the ring.

The Limitless Pendant is a brand new, AI-powered wearable that wants to do things a bit differently. Instead of trying to replace your smartphone like the Humane AI Pin, it’s more of an accessory designed to make video calls and in-person meetings easier to remember.

The device itself can either be clipped onto your shirt or worn around your neck like a necklace. Once turned on, the Limitless Pendant records everything you hear and then uses AI to transcribe conversations so that you can remember them later. However, Limitless takes things a step further by suggesting action items for your to-do list based on what was discussed during a meeting.

In the announcement video , it’s clear that Limitless thought a lot about privacy while designing its new AI wearable. Not only is your data anonymized and stored securely in the cloud, but you also need to get consent from others in your meetings before the Pendant can record their end of a conversation.

Price is another way in which Limitless is setting its Pendant apart from the competition. It starts at $99 during the preorder period , and while there is a subscription, there’s also a free tier that provides 10 hours of AI features per month along with unlimited audio storage. The $19 per month Pro plan removes this limit on AI features, and just like with audio storage, those are unlimited, too.The Limitless Pendant also comes in a number of different colors, from black to forest green to hot pink, so that it can blend in with your own personal style.

In terms of specs, the device has a 100-hour battery life and comes equipped with Wi-Fi and Bluetooth. It’s also waterproof, and you can charge it via USB-C. The Limitless Pendant is also built from aluminum and is just 1.25 inches across and 0.62 inches thick.

An AI wearable you might actually use

The Limitless Pin was just announced today (April 15) and will begin shipping this August, but in my opinion, it really seems like a much more useful gadget than some of the AI-powered devices we’ve seen so far.

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Paying attention during meetings is hard enough as it is with so much else going on in the background. With the Limitless Pendant though, you can be fully present during a meeting without having to worry about taking notes or adding things to your to do list. Likewise, the fact that there’s also a web app along with Mac and Windows software to go alongside the device seems promising, too. It’s one thing to have a device record all your conversations, but in order to be truly useful, you need to be able to access this data across devices when you need it most.

Another great thing about the Limitless Pendant is that it works with all of your existing tools and software. Unlike with add-ons for video calls where you need to use a particular app or program, you’ll be able to use this device with Zoom, Google Meet, Slack and probably most other video chat apps without having to install anything; you also won’t need to get approval from your company’s IT department first.

We plan on going hands-on with the Limitless Pin once it’s available, but based on what I’ve heard so far, it could certainly give the Humane AI Pin and the rabbit r1 a run for their money.

More from Tom's Guide

• Google could pump its Gemini AI chatbot directly into your ears 
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Anthony Spadafora is the security and networking editor at Tom’s Guide where he covers everything from data breaches and ransomware gangs to password managers and the best way to cover your whole home or business with Wi-Fi. Before joining the team, he wrote for ITProPortal while living in Korea and later for TechRadar Pro after moving back to the US. Based in Houston, Texas, when he’s not writing Anthony can be found tinkering with PCs and game consoles, managing cables and upgrading his smart home. 

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Meta, in Its Biggest A.I. Push, Places Smart Assistants Across Its Apps

Users of Instagram, Facebook, WhatsApp and Messenger will be able to turn to the new technology, powered by Meta’s latest artificial intelligence model, to obtain information and complete tasks.

• Share full article

By Mike Isaac and Cade Metz

Mike Isaac covers Meta and Silicon Valley. Cade Metz writes about artificial intelligence.

On a call with investors last spring, Mark Zuckerberg, the chief executive of Meta, said he believed that he had an opportunity to introduce artificially intelligent assistants “to billions of people in ways that will be useful and meaningful.”

A year later, he is making good on his statement.

On Thursday, Meta will begin incorporating new versions of its A.I.-powered smart assistant software across its apps, which include Instagram, WhatsApp, Messenger and Facebook. The latest technology will be rolled out in more than a dozen countries, including Australia, Canada, Singapore and the United States.

The A.I. software will become practically omnipresent — inside the news feed, in search bars and in chats with friends. People will be able to ask the assistant, Meta A.I., for help in completing tasks and getting information, such as what concerts might be occurring in San Francisco on a Saturday night or the best options for vegan enchiladas in New York.

Meta A.I. is powered by LLaMA 3, the company’s newest and most powerful large language model, an A.I. technology that can generate prose, conduct conversations and create images.

“With LLaMA 3, Meta A.I. will now be the most intelligent freely available assistant,” Mr. Zuckerberg said in an interview. “And because we’ve reached the quality level we want, we’re now going to make it much more prominent and easier to use across all our apps.”

The effort is Meta’s biggest rollout of products that include powerful A.I. technology. The social networking giant started weaving generative A.I. into its apps last year in a limited capacity, debuting a series of A.I.-powered chatbots and characters that could conduct conversations with users in September. But this new initiative exceeds that in scope and aim, placing A.I. products into the most visible and most used parts of Meta’s apps.

Other tech giants are also plugging A.I. into their products, as Silicon Valley start-ups raise billions of dollars to build A.I.-powered apps and services that they believe will define the next phase of computing.

Last year, Microsoft incorporated OpenAI’s ChatGPT into the software giant’s Bing search engine. Google has integrated A.I. into products like Docs, Gmail and Google Search. Start-ups such as Perplexity and Anthropic are also aiming to get more A.I.-powered products and services to consumers.

Meta’s efforts stand out because of the sheer scale of its products, which are used by nearly four billion people globally every month. It is also one of the few companies to “open source” most of the A.I. technology they are building, which means that anyone can look at the underlying tech and use it to build products or services for free.

• Meta A.I.'s new group chat features. Meta
• Meta A.I.'s new WhatsApp features. Meta
• Meta A.I.'s new Facebook features. Meta

Mr. Zuckerberg said the new A.I. rollout was part of Meta’s historical “playbook” of adding a feature to its apps “when we felt it was ready.” He pointed to products like Stories and Reels, two video and image products that appeared in Instagram, and how those were later amalgamated into Facebook and WhatsApp.

When ChatGPT arrived in late 2022 , wowing people with the way it answered questions, wrote term papers and generated computer code, the tech industry raced to build similar technology — even as the tools sometimes made mistakes and generated untruths.

Because of such flaws, OpenAI and other leading A.I. companies said they would not open source the underlying technology that powered these chatbots. (The New York Times has sued OpenAI and Microsoft, claiming copyright infringement of news content related to A.I. systems.)

Meta took a different tack. It open sourced the first version of LLaMA in February 2023, before releasing a more powerful version less than six months later. Other companies have followed, including Google and a prominent French start-up, Mistral . By open sourcing the technology, independent researchers and engineers everywhere can help spot problems in the technology and improve it, the companies have said.

“We have always believed in this principle and are happy to see that the industry is embracing the power of open source and the positive possibilities it can create,” Ahmad Al-Dahle, Meta’s vice president of generative A.I., said in an interview.

Mr. Dahle said LLaMA 3 had shown vast improvements over Meta’s previous large language models, calling it “significantly better” than what people were used to.

Meta has also fine-tuned the A.I. model to make it slightly less conservative in the type of questions Meta A.I. will answer, meaning the assistant will be less likely to refuse to answer some questions. In the past, Meta, Microsoft and others aimed to limit their chatbots from discussing third-rail topics like politics, religion and medical advice, fearing repercussions from political or interest groups.

To attract users, Meta will also add a faster image-generation technology into the A.I. assistant, and later plans to incorporate the A.I. tech into its Ray-Ban Meta smart glasses.

The challenge will be to convince people that the new assistants can be useful. Meta is working on helping people learn what kind of questions they can ask the assistants to bring them to life, Mr. Dahle said.

“Despite how prevalent these A.I. have become, there’s still an education factor on how to interact with an A.I.,” he said.

Like most of Meta’s products, the new assistants are free to use — and likely difficult to avoid if you are a regular user of the company’s apps.

Meta’s executives don’t appear worried about A.I. saturation. “We’re excited to share our next-generation assistant with even more people and can’t wait to see how it enhances people’s lives,” the company said.

Mike Isaac is a technology correspondent for The Times based in San Francisco. He regularly covers Facebook and Silicon Valley. More about Mike Isaac

Cade Metz writes about artificial intelligence, driverless cars, robotics, virtual reality and other emerging areas of technology. More about Cade Metz

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