The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index; none of the 18 sources its claims cite is checked for support yet.
ChatGPT and large language models in academia: opportunities and challenges
Open accessAbstract
The introduction of large language models (LLMs) that allow iterative “chat” in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic’s perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This editorial argues that LLM-based chatbots such as ChatGPT should be embraced in academia for writing, education, and programming, while cautioning about bias, hallucination, and poor accuracy. The authors conclude that LLM use does not constitute plagiarism, that bias metrics are needed, and that users remain responsible for validating outputs.
Key findings
The authors’ stance is that (1) LLMs must be embraced, (2) their use does not constitute plagiarism although they may produce plagiarized text, (3) their bias must be quantified, (4) users must be cautious of poor accuracy, and (5) the future is bright for their application to research and as an academic tool.
- ChatGPT can present false information as truth: when asked to help find relevant publications to cite, it “proceeded to make up a list of five entirely fictional publications”; on repeat trials it sometimes listed one or two real papers, but other times fabricated a paper or returned an irrelevant one.
- On scalability and accessibility, the paper reports ChatGPT gained 100 million active users in 2 months, with an estimated 13 million daily queries, compared with Google search’s estimated 8.5 billion daily queries; ChatGPT offers a free tier and a $20/month premium plan. It also notes BLOOM as an open access 176B parameter multilingual LLM, ChatGPT as a 175 billion parameter LLM, speculation that OpenAI had access to 10,000 Nvidia V100s costing close to $5 million, and an estimate that training BERT (described as a 6 billion parameter LLM) would produce 21 to 78 metric tons of CO2.
- For academic writing, LLMs may particularly help non-native English speakers; the paper cites that 49% of postdoctoral fellows trained in the United States were born overseas, and in engineering, math, and computer science, 60% of PhDs were awarded to international students. For programming, ChatGPT can help write, interpret, debug, simplify, and translate code, but it may present incorrect code as correct, miss edge cases, and is more reliable for smaller blocks than larger complex programs.
Methods
Design: editorial/commentary, not an empirical study or systematic review.
- Data or sample: no primary data; the paper draws on published literature, illustrative examples, and the authors’ experiences.
- Main analysis: narrative discussion of opportunities and challenges across academic writing, education, programming, bias, and scalability/accessibility.
Limitations
The paper does not report a formal limitations section for its own approach. Evident from the text: it is an editorial/commentary, so its examples are illustrative and include personal experiences rather than systematic evidence.
- The paper states LLM limitations: hallucination/false information, bias from training data, poor accuracy, misrepresentation of capabilities, and inability to anticipate edge cases in code.
- The paper notes unresolved ethical/legal uncertainty around LLM use in academic writing and grants, and that detection of cheating remains an unsolved challenge for educators.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
17 Own interpretation · 9 Not checked yet · 7 No basis given · 4 Own result
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A fundamental challenge with LLM-based chatbots is that they can present false information as truth, also known as “hallucination”.”
- Also stated in: § Background · plain
“For example, when asked if it could help find relevant publications to cite in a review paper, ChatGPT confirmed that it could, but then proceeded to make up a list of five entirely fictional publications.”
§ ChatGPT and large language models in academia: opportunities and challenges > Background
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Background · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In fact, ChatGPT and Bard can misrepresent their capabilities.”
§ ChatGPT and large language models in academia: opportunities and challenges > Background
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, LLMs may adopt bias, perpetuate stereotypes in the training dataset, and present false information as truth.”
§ ChatGPT and large language models in academia: opportunities and challenges > Introduction
What the sources say
- Not checked yetcites [3] Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare. 2023;11(6):887. https://doi.org/10.3390/healthcare11060887. source
- Not checked yetcites [4] Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2:e0000198. source
- Not checked yetcites [5] Liebrenz M, Schleifer R, Buadze A, Bhugra D, Smith A. Generating scholarly content with ChatGPT: ethical challenges for medical publishing. Lancet Digit Health. 2023;5:e105–6. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These issues limit the utility of LLM chatbots in some domains, such as the medical field, where facts are essential to ensure the best health outcomes.”
§ ChatGPT and large language models in academia: opportunities and challenges > Background
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, an answer generated by a large language model can be formatted correctly but not necessarily be factual.”
§ ChatGPT and large language models in academia: opportunities and challenges > Background
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Over reliance may hinder students ability to develop critical skills, such as writing [21].”
§ ChatGPT and large language models in academia: opportunities and challenges > Education
What the sources say
- Not checked yetcites [21] Shidiq M. The use of artificial intelligence-based chat-gpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills. Proc Int Conf Educ Soc Humanity. 2023;1(1):353–7. ISSN 2986-5832.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Consequently, employing LMMs with pre-written text is unlikely to raise ethical concerns such as plagiarism, which may arise when using prompt-based text generation by LMMs.”
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > ChatGPT as a writing and editing tool
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Unethically and irresponsibly designed AI/ML solutions deployed in the healthcare setting can exacerbate and perpetuate systematic biases and disparities for those from marginalized groups [27–29].”
§ ChatGPT and large language models in academia: opportunities and challenges > Bias
What the sources say
- Not checked yetcites [27] Vyas DA, Eisenstein LG, Jones DS. Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms. N Engl J Med. 2020;383:874–82. source
- Not checked yetcites [28] Gijsberts CM, Groenewegen KA, Hoefer IE, Eijkemans MJC, Asselbergs FW, Anderson TJ, et al. Race/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events. PLoS ONE. 2015;10:e0132321. source
- Not checked yetcites [29] Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting Racial Bias in an Algorithm used to Manage the Health of Populations. Science. 2019;366:447–53. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A recent study estimated that the training of BERT (a 6 billion parameter LLM) would produce between 21 to 78 metric tons of CO2 to train to completion [14].”
§ ChatGPT and large language models in academia: opportunities and challenges > Scalability and accessibility of LLMs
What the sources say
- Not checked yetcites [14] Dodge J, Prewitt T, Des Combes RT, Odmark E, Schwartz R, Strubell E, Luccioni AS, Smith NA, DeCario N, Buchanan W. Measuring the Carbon Intensity of AI in Cloud Instances. arXiv. 2022;2206.05229. source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Until the root-causes of the encoded biases of LLMs are addressed, LLMs for clinical applications will suffer the same fate, propagating biases [30, 31].”
§ ChatGPT and large language models in academia: opportunities and challenges > Bias
What the sources say
- Not checked yetcites [30] Neeley T, Ruper S. Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models. Harvard Business School Case. 2022;422–085.
- Not checked yetcites [31] Bender EM, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: can language models be too big? In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM; 2021. p. 610–23. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 23
- It may state two separate claims as one. 11
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In one author's experience experimenting with how ChatGPT might help write grants, ChatGPT can help provide some ideas for interesting directions, even suggesting sound logical reasons that we need to do research that could find their way into the significance section of NIH grants.”
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > ChatGPT and LLMs for grant proposal writing
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, grants must convey many complex ideas in a short amount of space, and as such, since much of the text from ChatGPT is so general and often repetitive, in our experience none of the text produced by ChatGPT made it into the final grant proposal.”
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > ChatGPT and LLMs for grant proposal writing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT can even provide general text that might be logically used in parts of the grant, such as the aims page.”
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > ChatGPT and LLMs for grant proposal writing
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A more important concern regarding students learning from a LLM is that it typically responds in a confident, and seemingly knowing manner, whether the provided information is accurate or not.”
§ ChatGPT and large language models in academia: opportunities and challenges > Education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It can suggest specific experiments that might test a hypothesis of interest.”
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > ChatGPT and LLMs for grant proposal writing
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A surprising effect for one author of this editorial was that it helped overcome writer's block in that simply having some text on the page was sufficient to move forward with editing.”
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > ChatGPT and LLMs for grant proposal writing
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The model can generate correctly formatted information such as references, that do not exist.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ChatGPT will misrepresent its knowledge; instead of responding “I don’t know”, it will readily provide fabricated information with apparent confidence.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, LLMs may adopt bias, perpetuate stereotypes in the training dataset, and present false information as truth.”
§ ChatGPT and large language models in academia: opportunities and challenges > Introduction
What the sources say
- Not checked yetcites [3] Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare. 2023;11(6):887. https://doi.org/10.3390/healthcare11060887. source
- Not checked yetcites [4] Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2:e0000198. source
- Not checked yetcites [5] Liebrenz M, Schleifer R, Buadze A, Bhugra D, Smith A. Generating scholarly content with ChatGPT: ethical challenges for medical publishing. Lancet Digit Health. 2023;5:e105–6. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, it would not be wise to base treatment plans on outputs from ChatGPT, at least in its current state, because this would lead to a large proportion of misdiagnoses and ineffective treatments that could cause patient harm.”
§ ChatGPT and large language models in academia: opportunities and challenges > Background
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Deploying AI models at scale is a challenge that future AI technologies will have to face even for tech giants such as Google (Bard power by LaMDA) and Microsoft (AI powered Bing) that have announced AI integration within their respective search engines.”
§ ChatGPT and large language models in academia: opportunities and challenges > Scalability and accessibility of LLMs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In line with warnings for general use, ChatGPT may somewhat unpredictably present incorrect code as being correct.”
§ ChatGPT and large language models in academia: opportunities and challenges > Assistance with programming
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Concerns have arisen regarding how students might easily use ChatGPT dishonestly, either to cheat in the completion of homework or exams, or to write reports/essays without citation in a manner that could be construed as plagiarism [22].”
§ ChatGPT and large language models in academia: opportunities and challenges > Education
What the sources say
- Not checked yetcites [22] Neumann M, Rauschenberger M, Schön E-M. “We Need To Talk About ChatGPT”: The Future of AI and Higher Education. 2023:4. https://doi.org/10.25968/opus-2467. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Currently, ChatGPT is more likely to be successful in accurately writing smaller blocks of code, whereas its reliability in writing larger/more complex programs (e.g. a software package) is questionable.”
§ ChatGPT and large language models in academia: opportunities and challenges > Assistance with programming
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Ultimately, code written by ChatGPT may offer a useful starting point, however both the code and comments that it generates should be checked and validated by both the programmer and users to confirm that it fully satisfies the intended purpose.”
§ ChatGPT and large language models in academia: opportunities and challenges > Assistance with programming
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT also promises to facilitate computer programming in terms of (1) learning to code or use specific packages, libraries, or frameworks, (2) writing new code, (3) interpreting existing code, (4) debugging existing code, (5) increasing the compactness or efficiency of code, or (6) translating code from one programming language to another.”
§ ChatGPT and large language models in academia: opportunities and challenges > Assistance with programming
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“LLMs provide benefits and opportunities in education for students by assisting with research and academic writing [15] or using them as an interactive study guide which may include generating practice exams and being provided immediate feedback [16, 17].”
§ ChatGPT and large language models in academia: opportunities and challenges > Education
What the sources say
- Not checked yetcites [15] Kasneci E, Sessler K, Küchemann S, Bannert M, Dementieva D, Fischer F, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn Individ Differ. 2023;103:102274. source
- Not checked yetcites [16] MacNeil S, Tran A, Mogil D, Bernstein S, Ross E, Huang Z. Generating Diverse Code Explanations using the GPT-3 Large Language Model. In: Vahrenhold J, Fisler K, Hauswirth M, Franklin D, editors. Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2. New York, NY, USA: ACM; 2022. p. 37–9. source
- Not checked yetcites [17] Tate TP, Doroudi S, Ritchie D, Xu Y, Mark Warschauer UCI. Educational Research and AI-Generated Writing: Confronting the Coming Tsunami. 2023.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While AI has the potential to revolutionize education and academic research, paywalled access could further increase the divide between the wealthy and the poor.”
§ ChatGPT and large language models in academia: opportunities and challenges > Scalability and accessibility of LLMs
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: AI has the potential to revolutionize education and academic research.
In the paper
“Data produced from ChatGPT may be inaccurate or biased [23], which users need to be aware of to ensure these deficiencies are not propagated in their works.”
§ ChatGPT and large language models in academia: opportunities and challenges > Education
What the sources say
- Not checked yetcites [23] Baidoo-Anu D, Owusu Ansah L. Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. 2023. Available at SSRN: https://ssrn.com/abstract=4337484 or https://doi.org/10.2139/ssrn.4337484. source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This editorial provides several examples and perspectives on how LLMs increase the efficiency of academic writing, education, and programming.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In many contexts, LLMs have low accuracy.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Output from ChatGPT will match existing text on the internet, particularly if the sample is small enough.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: The output of ChatGPT is from most publicly available text on the internet until 2021.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, ChatGPT does not do anything without a prompt.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When LLMs for clinical applications are socially, consciously, and transparently designed with such considerations, they can become an additional tool for promoting equity and improving access to care.”
§ ChatGPT and large language models in academia: opportunities and challenges > Bias
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The performance of ChatGPT and other LLMs therefore mimics the available text, and they are as biased as their training data.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The output of ChatGPT is from most publicly available text on the internet until 2021.
- Rests on an assumption: ChatGPT is known to perpetuate stereotypes such as nurses being female and doctors being male, and this bias comes from the training data.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, in a strict sense, LLMs like ChatGPT do produce plagiarism.”
§ ChatGPT and large language models in academia: opportunities and challenges > Conclusion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: The output of ChatGPT is from most publicly available text on the internet until 2021.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As companies race to innovate with LLMs like ChatGPT and others, encoded biases will be amplified, and harm will manifest.”
§ ChatGPT and large language models in academia: opportunities and challenges > Bias
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
Effective academic writing is essential to the success and quality of academic publications and is a major challenge facing international postdocs and students.
“Effective academic writing plays an essential role in the success and quality of academic publications, nevertheless, it is one of the major challenges facing international postdocs and students.”
The paper relies on this without support.
The current grant review system is subjective; some proposals scored very poorly in one study section while nearly the same proposal at another study section is funded.
“Despite this massive investment, the current review system is subjective; for example, some proposals sent to one study section are scored very poorly, while nearly the same proposal at another study section is funded.”
The paper relies on this without support.
ChatGPT has already been applied to help write grant proposals.
“ChatGPT has undoubtedly already been applied to help write grant proposals.”
The paper relies on this without support.
ChatGPT is a 175 billion parameter LLM.
“As a reminder, ChatGPT is a 175 billion parameter LLM and this only considers the cost of training and not any cost related to generating text in response to potentially billions of daily queries.”
The paper relies on this without support.
The output of ChatGPT is from most publicly available text on the internet until 2021.
“The output of ChatGPT is from most publicly available text on the internet until 2021.”
The paper relies on this without support.
ChatGPT is known to perpetuate stereotypes such as nurses being female and doctors being male, and this bias comes from the training data.
“For example, ChatGPT is known to perpetuate stereotypes such as nurses being female and doctors being male, and this bias comes from the training data.”
The paper relies on this without support.
The use of ChatGPT and other LLMs by students is inevitable.
“As the use of ChatGPT and other LLMs by students is inevitable, educators will need to change the pedagogy and assessments in education, such as integrating the use of ChatGPT in lessons [26], and a focus more on the process rather than the final product for assessment.”
The paper relies on this without support.
Detection of cheaters remains an unsolved challenge for educators.
“While the detection of cheaters remains an unsolved challenge for educators, if we start from the assumption that students are allowed, if not encouraged, to utilize ChatGPT, strategies could be adopted to account for this.”
The paper relies on this without support.
It is difficult to define metrics that assess the level of bias in training data and model outputs.
“It is difficult to define metrics that assess the level of bias in the training data and in the model outputs.”
The paper relies on this without support.
AI has the potential to revolutionize education and academic research.
“While AI has the potential to revolutionize education and academic research, paywalled access could further increase the divide between the wealthy and the poor.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“using GenAI to automate routine administrative tasks in higher education, such as consolidation of grades, creating performance summaries of students, crafting out evaluation reports, and maintaining student enrolment and attendance records (Meyer et al., 2023).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Meyer et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Third, LLMs can act as a thought partner for students and enhance understanding by providing detailed explanations and step-by-step solutions (Mollick and Mollick 2022, Kasneci et al. 2023, Meyer et al. 2023, Extance 2023).”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Meyer et al. 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“there has been a notable upswing in discussions among scholars and academicians regarding the transformative potential of generative AI, particularly ChatGPT, in higher education and the risks associated with it (Dalalah & Dalalah, 2023; Meyer et al., 2023; Peters et al., 2023; Yilmaz & Yilmaz, 2023a).”
From Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students (2024) · cites this paper as Meyer et al., 2023
In the citing paper
“The consequence of a self-referential loop in LLMs can lead to several problematic outcomes, including the propagation of biases [30], increased homogeneity in generated data, and ultimately, hindered performance.”
From Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Health Care Professionals (2024) · cites this paper as [30]
In the citing paper
“ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [16] with an estimated 13 million daily queries [49], surpassing all other applications [64].”
From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [49]
In the citing paper
“a fundamental challenge with LLM-based chatbots, including ChatGPT, is the accuracy and quality of the provided information and responses, as they provide false information as truth—a phenomenon often referred to as “hallucination” [3, 49]”
From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [49]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ChatGPT and large language models in academia: opportunities and challenges > ChatGPT for academic writing > Ethics and rules of using text from ChatGPT
“In one case, ChatGPT was listed as an author in the preprint [9] but missing as an author in the final publication [4].”
§ ChatGPT and large language models in academia: opportunities and challenges > References
“9. Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health. 2023;2(2):e0000198. 10.1371/journal.pdig.0000198. PMC9931230 36812645”
Citations
Cites
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“LLMs provide benefits and opportunities in education for students by assisting with research and academic writing [15] or using them as an interactive study guide which may include generating practice exams and being provided immediate feedback [16, 17].”
BackgroundDissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 1×
“Unethically and irresponsibly designed AI/ML solutions deployed in the healthcare setting can exacerbate and perpetuate systematic biases and disparities for those from marginalized groups [27–29].”
BackgroundOn the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · cited 1×
“In one case, ChatGPT was listed as an author in the preprint [9] but missing as an author in the final publication [4].”
BackgroundBAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · cited 1×
“On the other hand, there are also unsettling ethical issues to be considered [3–5].”
BackgroundChatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · cited 1×
“Data produced from ChatGPT may be inaccurate or biased [23], which users need to be aware of to ensure these deficiencies are not propagated in their works.”
Background
Research works printed but not linked: 4
- Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health. 2023;2(2):e0000198. https://doi.org/10.1371/journal.pdig.0000198. · DOI · Paper page
- Shidiq M. The use of artificial intelligence-based chat-gpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills. Proc Int Conf Educ Soc Humanity. 2023;1(1):353–7. ISSN 2986-5832.
- Trust T, Whalen J, Mouza C. Editorial: ChatGPT: Challenges, opportunities, and implications for teacher education. Contemp Issues Technol Teacher Educ. 2023;23(1):1–23.
- Dijkstra R, Genç Z, Kayal S, Kamps J. Reading Comprehension Quiz Generation using Generative Pre-trained Transformers. 2022.
Cited by
Sorted by citations
Using artificial intelligence in academic writing and research: An essential productivity tool
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100145 · Cited by 580
Impact of AI assistance on student agency
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2023.104967 · Cited by 548
“…educators in various aspects of pedagogy, enhancing productivity, generating detailed feedback, and reshaping the peer review process while also raising noteworthy considerations related to ethics, biases, and transparency (Cooper, 2023; Donker, 2023; Li, Patel, & Du, 2023; Meyer et al., 2023).”
BackgroundAbbas, M., Jam, F. A., & Khan, T. I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00444-7 · Cited by 531
“…and its potentially harmful or beneficial consequences is critical for educators, policymakers, and students, as it can help the development of effective strategies to integrate generative AI technologies into the learning process and control their misuse in higher education (Meyer et al., 2023).”
BackgroundA multinational study on the factors influencing university students’ attitudes and usage of ChatGPT
Abdaljaleel, M., Barakat, M. M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D. N., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52549-8 · Cited by 391
“Several previous studies indicated the potential utility of ChatGPT as a prime example among other LLMs in higher education 1,18,26,63–65 .”
BackgroundMustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235
“Recent studies have highlighted the opportunities that these techniques can bring in teaching and learning but also noted the challenges and the barriers that need to be addressed (Kas-neci et al., 2023; Meyer et al., 2023).”
AI Tutoring Outperforms Active Learning
Kestin*, G., Miller*, K., Klales, A., Milbourne, T., & Ponti, G. (2024) · doi:10.21203/rs.3.rs-4243877/v1 · Cited by 214
A systematic literature review of empirical research on ChatGPT in education
Albadarin, Y., Saqr, M., Pope, N., & Tukiainen, M. (2024) · Discover Education · doi:10.1007/s44217-024-00138-2 · Cited by 191
“ChatGPT is considered the fastest-growing technology in history: in just three months following its public launch, it amassed an estimated 120 million monthly active users [16] with an estimated 13 million daily queries [49], surpassing all other applications [64].”
InfluentialKestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-97652-6 · Cited by 214
Choudhury, A., & Chaudhry, Z. (2024) · Journal of Medical Internet Research · doi:10.2196/56764 · Cited by 159
“The consequence of a self-referential loop in LLMs can lead to several problematic outcomes, including the propagation of biases [30], increased homogeneity in generated data, and ultimately, hindered performance.”
A Design Space for Intelligent and Interactive Writing Assistants
Lee, M., Gero, K. I., Chung, J. J. Y., Shum, S. B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E. A., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642697 · Cited by 136
Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania
Rudolph, J., Mohamed Ismail, F. M., & Popenici, S. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/54fs5e77 · Cited by 189
“…processes (Javaid et al., 2023), using GenAI to automate routine administrative tasks in higher education, such as consolidation of grades, creating performance summaries of students, crafting out evaluation reports, and maintaining student enrolment and attendance records (Meyer et al., 2023).”
MethodBest Practices for Using AI Tools as an Author, Peer Reviewer, or Editor
Leung, T. I., de Azevedo Cardoso, T., Mavragani, A., & Eysenbach, G. (2023) · Journal of Medical Internet Research · doi:10.2196/51584 · Cited by 127
“At JMIR Publications, we adhere to this guidance of transparency and disclosure; we do not endorse a ban on generative AI in peer review, which can be counterproductive in various ways [14,35].”
Jo, H. (2023) · Telematics and Informatics · doi:10.1016/j.tele.2023.102067 · Cited by 131
Evangelista, E. D. L. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/15775 · Cited by 124
“Educators are growingly concerned that students will employ ChatGPT to create written assignments (Halaweh, 2023; Meyer et al., 2023).”
Large Language Model‐Based Chatbots in Higher Education
Yigci, D., Eryilmaz, M., Yetisen, A. K., Tasoglu, S., & Ozcan, A. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400429 · Cited by 113
Miao, J., Thongprayoon, C., Suppadungsuk, S., Garcia Valencia, O. A., Qureshi, F., & Cheungpasitporn, W. (2023) · Clinics and Practice · doi:10.3390/clinpract14010008 · Cited by 111
“Rather than debating whether AI should be used, the primary focus should revolve around how it can be harnessed responsibly and effectively [74].”
BackgroundPutman, T., Schaper, K., Matentzoglu, N., Rubinetti, V., Alquaddoomi, F. S., Cox, C., Caufield, J., Elsarboukh, G., Gehrke, S., Hegde, H. B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1082 · Cited by 116
Bias in Machine Learning: A Literature Review
Mavrogiorgos, K., Kiourtis, A., Mavrogiorgou, A., Menychtas, A., & Kyriazis, D. (2024) · Applied Sciences · doi:10.3390/app14198860 · Cited by 120
An analysis of large language models: their impact and potential applications
Bharathi Mohan, G., Prasanna Kumar, R., Vishal Krishh, P., Keerthinathan, A., Lavanya, G., Meghana, M. K. U., Sulthana, S., & Doss, S. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02120-8 · Cited by 99
Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation
Sufi, F. K. (2024) · Information · doi:10.3390/info15020099 · Cited by 127
Large Language Models in Computer Science Education: A Systematic Literature Review
Raihan, M. N., Siddiq, M. L., Santos, J. C. S., & Zampieri, M. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701863 · Cited by 162
“However, current methods, including plagiarism detection software, often fail to deliver reliable performance when handling output from a recently introduced LLM [7].”
Chen, K., Tallant, A. C., & Selig, I. (2025) · Information and Learning Sciences · doi:10.1108/ils-10-2023-0160 · Cited by 104
Exploring the role of large language models in the scientific method: from hypothesis to discovery
Zhang, Y., Khan, S. A., Mahmud, A., Yang, H., Lavin, A., Levin, M. G., Frey, J. G., Dunnmon, J., Evans, J., Bundy, A., et al. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00019-5 · Cited by 160
Large Language Model Prompting Techniques for Advancement in Clinical Medicine
Shah, K., Xu, A. Y., Sharma, Y. K., Daher, M., McDonald, C. L., Diebo, B. G., & Daniels, A. H. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13175101 · Cited by 96
“…can take several measures, including clearly stating the way LLM tools were used in the research process, ensuring that human oversight was utilized in the final output, including fact-checking and utilizing as many open-source AI models as possible to ensure reproducibility of information [40].”
MethodShaping generative AI governance in higher education: Insights from student perception
Barus, O. P., Hidayanto, A. N., Handri, E. Y., Sensuse, D. I., & Yaiprasert, C. (2025) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2025.100452 · Cited by 93
Πέλλας, Ν. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00276-4 · Cited by 120
“Given the growing significance of video content in online consumer experiences, it is crucial to comprehend how generative AI will reshape online video platforms and the creative industries at large (Fuchs, 2023; Meyer et al., 2023).”
BackgroundCurrent status of ChatGPT use in medical education: potentials, challenges, and strategies
Xu, T., Weng, H., Liu, F., Yang, L., Luo, Y., Ding, Z., & Wang, Q. (2024) · Journal of Medical Internet Research · doi:10.2196/57896 · Cited by 84
“Encouraging students to preserve transcripts of their interactions using ChatGPT can also help [31].”
Naeem, M., Smith, T., & Thomas, L. (2025) · International Journal of Qualitative Methods · doi:10.1177/16094069251333886 · Cited by 152
“The use of AI in qualitative research expedites the data analytical process (Prescott et al., 2024), but it also raises issues of conceptuality, ethics and transparency (Meyer et al., 2023; Salimi & Saheb, 2023).”
Artificial Intelligence Integration: Pedagogical Strategies and Policies at Leading Universities
Alqahtani, N., & Wafula, Z. (2025) · Innovative Higher Education · doi:10.1007/s10755-024-09749-x · Cited by 84
“AI Large Language models, such as ChatGPT, Bard, and Claude, have enhanced the educator’s pedagogical productivity (Li et al., 2024; Meyer et al., 2023 ).”
Chow, J. C. L., & Li, K. (2024) · JMIR Bioinformatics and Biotechnology · doi:10.2196/64406 · Cited by 75
Pushpanathan, K., Lim, Z. W., Yew, S. M. E., Chen, D. Z., Lin, H. A. H., Goh, J. H. L., Wong, W., Wang, X., Tan, M. C. J., Koh, V., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108163 · Cited by 82
Data extraction from polymer literature using large language models
Gupta, S., Mahmood, A. U., Shetty, P., Adeboye, A., & Ramprasad, R. (2024) · Communications Materials · doi:10.1038/s43246-024-00708-9 · Cited by 103
“However, obtaining the desired output from an LLM poses a challenging task and is presently a subject of active research 33,34 .”
Large language models for life cycle assessments: Opportunities, challenges, and risks
Preuss, N., Alshehri, A. S., & You, F. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.142824 · Cited by 71
Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00547-9 · Cited by 69
“…importance of understanding learners’ trust in GenAI should not be overstated in the current era of GenAI, especially when a growing body of research is highlighting students’ disuse, misuse, and abuse of GenAI in education (e.g., Klingbeil et al., 2024; Meyer et al., 2023; Shahzad et al., 2024).”
InfluentialPudasaini, S., Miralles-Pechuán, L., Lillis, D., & Llorens Salvador, M. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-024-09576-x · Cited by 70
“One major concern is determining authorship and intellectual property rights for texts generated with LLMs, particularly in collaborative environments where LLMs might contribute partially (Meyer et al., 2023).”
Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT
Akhavan, A., & Jalali, M. S. (2024) · System Dynamics Review · doi:10.1002/sdr.1773 · Cited by 68
Introduction to Artificial Intelligence
Papajorgji, P. J., & Moskowitz, H. (2024) · Springer eBooks · doi:10.1007/978-3-031-78078-3_5 · Cited by 66
Kooli, C., & Yusuf, N. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2338330 · Cited by 63
“For these reasons, the decision to integrate or restrict AI technologies like ChatGPT in educational settings should be based on a well-informed scholarly consensus that would consider evidence bases from various fields (Kooli, 2023b; Meyer et al., 2023).”
Symeou, L., Louca, L. Τ., Kavadella, A., Mackay, J., Danidou, Y., & Raffay, V. I. (2025) · European Journal Of Dental Education · doi:10.1111/eje.13069 · Cited by 87
A personal health large language model for sleep and fitness coaching
Khasentino, J., Belyaeva, A., Liu, X., Yang, Z., Furlotte, N. A., Lee, C., Schenck, E., Patel, Y., Cui, J., Schneider, L. D., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03888-0 · Cited by 151
“Few-shot prompting with self-consistency improved performance further in both sleep and fitness examinations (Supplementary Table 6).”
Analyzing student prompts and their effect on ChatGPT’s performance
Sawalha, G., Taj, I., & Shoufan, A. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2397200 · Cited by 93
“However, the acceptance of LLMs in education - particularly in Engineering and computation - will depend on their accuracy and response correctness (Meyer et al., 2023), which in turn relies on the quality of the prompt (Knoth et al., 2024; Stojanov et al., 2024; Wang et al., 2024; White et al.,…”
Idris, M. D., Feng, X., & Dyo, V. (2024) · IEEE Access · doi:10.1109/access.2024.3400164 · Cited by 71
Zhu, S., Wang, Z., Zhuang, Y., Jiang, Y., Guo, M., Zhang, X., & Gao, Z. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100138 · Cited by 65
Comparing GPT-4 and Human Researchers in Health Care Data Analysis: Qualitative Description Study
Li, K. D., Fernandez, A., Schwartz, R., Ríos, N., Carlisle, M., Amend, G., Patel, H. V., & Breyer, B. N. (2024) · Journal of Medical Internet Research · doi:10.2196/56500 · Cited by 65
“These developments raise critical questions about their potential and ethical use [1-3].”
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
Mroz, A. M., Basford, A. R., Hastedt, F., Jayasekera, I. S., Mosquera‐Lois, I., Sedgwick, R., Ballester, P. J., Bocarsly, J. D., del Rio‐Chanona, E. A., Evans, M. L., et al. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00146c · Cited by 61
Rane, N. L. (2024) · International Journal of Artificial Intelligence and Machine Learning · doi:10.51483/ijaiml.4.1.2024.22-47 · Cited by 68
Evolution of Learning: Assessing the Transformative Impact of Generative AI on Higher Education
Krause, S., Panchal, B. H., & Ubhe, N. (2025) · Frontiers of digital education. · doi:10.1007/s44366-025-0058-7 · Cited by 80
Zhou, J., Zhang, J., Wan, R., Cui, X., Liu, Q., Guo, H., Shi, X., Fu, B., Meng, J., Yue, B., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06916-2 · Cited by 61
Watson, S., & Romic, J. (2025) · European Educational Research Journal · doi:10.1177/14749041231221266 · Cited by 50
“This approach mitigates extreme utopian or dystopian perspectives, guiding a more nuanced discourse around the responsible integration of LLMs into educational and social systems (Meyer et al., 2023).”
Ma, Y., Lan, Y., Xie, Y., Yu, L., Chen, C., Wu, Y., & Dai, X. (2024) · Remote Sensing · doi:10.3390/rs16020404 · Cited by 53
“BERT [38] and GPT [39], among other NLP models, have demonstrated the powerful learning capabilities of transformer, which has also attracted researchers to explore the application of transformer in computer vision tasks.”
Bjelobaba, S., Waddington, L., Perkins, M., Foltýnek, T., Bhattacharyya, S., & Weber-Wulff, D. (2025) · International Journal for Educational Integrity · doi:10.1007/s40979-025-00191-w · Cited by 96
“The integration of GenAI tools into this complex writing process requires careful consideration and potentially new guidelines from funding agencies (Meyer et al. 2023) to ensure research integrity.”
Artificial intelligence-powered tools and academic writing: to use or not to use ChatGPT
Al-Sofi, B. (2024) · Saudi Journal of Language Studies · doi:10.1108/sjls-06-2024-0029 · Cited by 45
Harnessing generative artificial intelligence to support nature‐based solutions
Richards, D. R., Worden, D., Song, X. P., & Lavorel, S. (2024) · People and Nature · doi:10.1002/pan3.10622 · Cited by 59
Faisal, E. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1325601 · Cited by 44
Pereira, E., Nsair, S., Pereira, L. R., & Grant, K. A. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00457-2 · Cited by 74
“Furthermore, given the risks associated with the misuse of AI (IEEE, n.d.) as well as equity, diversity, and inclusion (EDI) concerns (Meyer et al., 2023) associated with LLMs, users should be aware that the output from these models can be biased and inaccurate.”
BackgroundApplications of ChatGPT in Otolaryngology–Head Neck Surgery: A State of the Art Review
Lechien, J. R., & Rameau, A. (2024) · Otolaryngology · doi:10.1002/ohn.807 · Cited by 42
Sinha, R., Solola, I., Nguyen, H., Swanson, H., & Lawrence, L. (2024) · Symposium on Learning, Design and Technology (LDT) · doi:10.1145/3663433.3663456 · Cited by 42
Impact of Large Language Models on Medical Education and Teaching Adaptations
Zhui, L., Yhap, N., Liping, L., Wang, Z., Zhonghao, X., Xiaoshu, Y., Hong, C., Liu, X., & Wei, R. (2024) · JMIR Medical Informatics · doi:10.2196/55933 · Cited by 55
“This analytical capability fosters a more nuanced understanding of student performance, thus enabling educators to tailor their teaching strategies effectively [25].”
BackgroundDo AI-generative tools kill or nurture creativity in EFL teaching and learning?
Alzubi, A. A. F., Nazim, M., & Alyami, N. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13409-8 · Cited by 43
“Kohnke et al. ( 2023 ) and Meyer et al. ( 2023 ) also emphasized that, despite their abil - ity to generate creative content, AI tools cannot replace the rich, interactive nature of human collaboration, which is vital for deeper creativity.”
Busuttil, L., & Calleja, J. (2025) · Digital Experiences in Mathematics Education · doi:10.1007/s40751-024-00168-3 · Cited by 59
“Our findings resonate with studies highlighting ChatGPT’s potential in personalising learning and providing immediate feedback, aligning with Memarian and Doleck (2023) and Meyer et al. (2023), who stress the significance of individualised learning trajectories and interactive revision aids.”
InfluentialChatGPT as an AI L2 teaching support: A case study of an EFL teacher
Octavio, M. M., Argüello, M. V. G., & Pujolà, J. (2024) · Technology in Language Teaching & Learning · doi:10.29140/tltl.v6n1.1142 · Cited by 39
Creative Applications of Artificial Intelligence in Education
Urmeneta, A., & Roméro, M. (2024) · Palgrave studies in creativity and culture · doi:10.1007/978-3-031-55272-4 · Cited by 57
Bhattacharya, M., Pal, S., Chatterjee, S., Lee, S., & Chakraborty, C. (2024) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2024.102255 · Cited by 40
Human versus machine: The effectiveness of ChatGPT in automated essay scoring
Manning, J., Baldwin, J., & Powell, N. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2469089 · Cited by 36
“Additionally, educators must be vigilant regarding the potential ethical implications of using AI in grading, including the transparency of AI systems, the possibility of bias, and the fairness of grading practices (Meyer et al., 2023).”
Al-Abri, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13484-x · Cited by 52
“The inclusion of practice quizzes and detailed explanations supports the development of self-regulated learning strategies, and reduces exam-related anxiety as students better gauge their preparation levels (Cheung et al., 2023; Meyer et al., 2023).”
A Practical Guide and Assessment on Using ChatGPT to Conduct Grounded Theory: Tutorial
Yue, Y., Liu, D., Lv, Y., Hao, J., & Cui, P. (2025) · Journal of Medical Internet Research · doi:10.2196/70122 · Cited by 40
Uppal, K., & Hajian, S. (2024) · European Journal of Educational Research · doi:10.12973/eu-jer.14.1.199 · Cited by 36
Large language models and medical education: a paradigm shift in educator roles
Zhui, L., Li, F., Fu, Q., Wang, X., Liu, H., Zhao, Y., & Wei, R. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00313-w · Cited by 40
“The employment of LLMs streamlines the exam preparation process, conserves valuable time, and, by analysing students’ learning data and examination results, aids educators in discerning students’ learning patterns and challenges to refine teaching strategies (Meyer et al., 2023).”
BackgroundPirzado, F. A., Ahmed, A., Mendoza-Urdiales, R. A., & Terashima‐Marín, H. (2024) · IEEE Access · doi:10.1109/access.2024.3443621 · Cited by 41
“These findings align with some of the studies included in this review [28], [62].”
ResultConstruction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network
Wang, Y., Liu, J., Wang, W., Chen, J., Yang, X., Sang, L., Wen, Z., & Peng, Q. (2024) · Applied Sciences · doi:10.3390/app14188231 · Cited by 34
Anson, D. W. J. (2024) · Higher Education Research & Development · doi:10.1080/07294360.2024.2332259 · Cited by 56
Zhu, Y., Zhu, C., Wu, T., Wang, S., Zhou, Y., Chen, J., Wu, F., & Li, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12898-3 · Cited by 33
“They have the potential to revolutionize various facets of human daily life, including education (Meyer et al., 2023; Tamkin et al., 2021; Rillig et al., 2023).”
InfluentialKavitha, K., Joshith, V. P., & Sharma, S. (2024) · E-Learning and Digital Media · doi:10.1177/20427530241259099 · Cited by 49
“…described a wide range of assistance, including guidance on managing tasks, generating assignment ideas, explaining concepts in depth, offering new learnings, and providing systematic guidance on completing important tasks to enhance productivity (Javaid et al., 2023b; Meyer et al., 2023).”
Integrating vision‐based AI and large language models for real‐time water pollution surveillance
Samuel, R. D. J., Sermet, Y., Cwiertny, D. M., & Demir, İ. (2024) · Water Environment Research · doi:10.1002/wer.11092 · Cited by 36
“Meyer et al. ( 2023) investigates the potential use of LLM based chatbots in academic works and ethical implication (Sajja, Sermet, Cwiertny, et al., 2023b).”
Lin, Y., Luo, Z., Ye, Z., Zhong, N., Zhao, L., Zhang, L., Li, X., Chen, Z., & Chen, Y. (2025) · JMIR Medical Education · doi:10.2196/71125 · Cited by 31
“…a critical technological imbalance: the overwhelming focus on general-purpose LLMs like ChatGPT contrasts sharply with the lack of systematic development of specialized medical models and the near absence of research on multimodel collaborative mechanisms within medical education [10,164].”
What Does ChatGPT Mean for Qualitative Health Research ?
van Manen, M. (2023) · Qualitative Health Research · doi:10.1177/10497323231210816 · Cited by 31
“…2023; Baker et al., 2023; Cohen et al., 2023; Conroy et al., 2023; Dergaa et al., 2023; Dien, 2023; Doshi et al., 2023; Doyal et al., 2023; Farina & Lavazza, 2023; Garcia, 2023; Jeyaraman et al., 2023; Leung et al., 2023; Meyer et al., 2023; Ruf fl e et al., 2023; Takeda, 2023; Wang et al., 2023).”
Popkov, A. A., & Barrett, T. S. (2024) · Accountability in Research · doi:10.1080/08989621.2024.2331757 · Cited by 40
“Similarly, generative AI models like ChatGPT now provide a new level of support in writing but also cause new debates about academic authorship and integrity (DuBose and Marshall 2023; Else 2023; Flanagin et al. 2023; Meyer et al. 2023).”
Prandner, D., Wetzelhütter, D., & Hese, S. (2025) · Frontiers in Education · doi:10.3389/feduc.2024.1417900 · Cited by 45
Fontoura, L., Nascimento, D. L. D. M., Neto, J. V., & Caiado, R. G. G. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102847 · Cited by 43
“…and bias mitigation, which studies caution against the unscrupulous and irresponsible design of Gen-AI technologies, as such practices may perpetuate systemic prejudices and disparities, particularly affecting marginalised groups and potentially exacerbating existing biases (Meyer et al., 2023).”
Alyasiri, O. M., Salisu, S., Younis, H., Salman, A. M., Sahib, T. M., Akhtom, D., & Hayder, I. M. (2024) · Journal of Stomatology Oral and Maxillofacial Surgery · doi:10.1016/j.jormas.2024.101842 · Cited by 31
Huo, B., Calabrese, E., Sylla, P., Kumar, S., Ignacio, R. C., Oviedo, R., Hassan, I., Slater, B. J., Kaiser, A., Walsh, D. S., et al. (2024) · Surgical Endoscopy · doi:10.1007/s00464-024-10807-w · Cited by 28
Luo, C., Mao, B., Wu, Y., & He, Y. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106321 · Cited by 35
Large Language Models in Pediatric Education: Current Uses and Future Potential
Suresh, S., & Misra, S. M. (2024) · PEDIATRICS · doi:10.1542/peds.2023-064683 · Cited by 30
Global retractions due to randomly generated content: Characterization and trends
Lei, F., Du, L., Dong, M., & Liu, X. (2024) · Scientometrics · doi:10.1007/s11192-024-05172-3 · Cited by 35
“Nevertheless, the fundamental challenge is that they can present false information as truth, also known as “illusions” (Meyer et al., 2023).”
Hassan, A., Nizam‐Uddin, N., Quddus, A., Hassan, S. R., Rehman, A. U., & Bharany, S. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.057877 · Cited by 35
Prompt engineering: The next big skill in rheumatology research
Venerito, V., Lalwani, D., Vescovo, S. D., Iannone, F., & Gupta, L. (2024) · International Journal of Rheumatic Diseases · doi:10.1111/1756-185x.15157 · Cited by 27
ChatGPT: a transformative role in academia–insights into academic staff performance since adoption
Sadallah, M., Bin‐Nashwan, S. A., & Benlahcene, A. (2024) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-07-2024-0097 · Cited by 30
Alomari, E. A. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.052256 · Cited by 37
Mathebula, M., Modupe, A., & Marivate, V. N. (2024) · IEEE Access · doi:10.1109/access.2024.3464374 · Cited by 32
“ChatGPT is one of the largest language models and contains the most parameters, with 175 billion [57].”
BackgroundSaleh, Y., Talib, M. W. A., Nasir, Q., & Dakalbab, F. M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1523699 · Cited by 46
Machine Learning for Organic Fluorescent Materials
Zhong, J., Zhu, W., Shen, S., Zhou, N., Xi, M., Du, K., Wang, D., & Tang, B. Z. (2025) · Aggregate · doi:10.1002/agt2.70089 · Cited by 26
“Although data collection can be tedious, the rise of artificial intelligence, particularly Large Language Models (LLMs) [39], has facilitated the use of automated tools for data mining.”
A Systematic Literature Review of Empirical Research on ChatGPT in EducationPreprint
Albadarin, Y., Tukiainen, M., Saqr, M., & Pope, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4562771 · Cited by 26
Kortemeyer, G., & Nöhl, J. (2025) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.21.010136 · Cited by 48
“Most recently, Large Language Models (LLMs), in particular generative Pre-trained Transformers (GPT) [1], such as GPT-4 [2], have demonstrated impressive abilities in solving and evaluating academic tasks [3], including tasks related to academic education [4].”
Teaching and testing in the era of text-generative AI: exploring the needs of students and teachers
Jochim, J., & Lenz-Kesekamp, V. (2024) · Information and Learning Sciences · doi:10.1108/ils-10-2023-0165 · Cited by 32
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Du, J., Wang, Y., Zhao, W., Deng, Z., Liu, S., Lou, R., Zou, H. P., Narayanan Venkit, P., Zhang, N., Srinath, M., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.292 · Cited by 23
Schnepper, R., Roemmel, N., Schaefert, R., Lambrecht-Walzinger, L., & Meinlschmidt, G. (2024) · JMIR Mental Health · doi:10.2196/57986 · Cited by 28
“This may not only facilitate the automatic analysis of large amounts of text data [9,10] but, by modeling words in a large context, also allow the generation of meaningful text and the interactive use of this technology [5,10].”
Ballard, D. H., Antigua-Made, A., Barré, E., Edney, E. A., Gordon, E. B., Kelahan, L. C., Lodhi, T., Martin, J. G., Özkan, M. U., Serdynski, K. C., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.10.023 · Cited by 25
Aloudat, M. Z., Aboumadi, A., Soliman, A., Al-Mohammed, H. A., Al-Ali, M. S., Mahgoub, A., Barhamgi, M., & Yaacoub, E. E. (2025) · IEEE Access · doi:10.1109/access.2025.3555753 · Cited by 31
“This involves speaking in a way appropriate for the characters, setting, and intended tone—all of which might change greatly throughout virtual worlds [175], [252]. d) Scalability: Scalability is an issue for LLMs in the metaverse, just like it is with blockchain [253], [254].”
Gao, Y., Zhaı, X., Li, M., Lee, G., & Liu, X. (2025) · Journal of Research in Science Teaching · doi:10.1002/tea.70009 · Cited by 34
Jamshed, M., Ahmed, A. S. M. M., Sarfaraj, M., & Warda, W. U. (2024) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v18i19.50361 · Cited by 24
Metadata
- Authors
- Meyer, J. G., Urbanowicz, R. J., Martin, P. C. N., O’Connor, K., Li, R., Peng, P.-C., Bright, T. J., Tatonetti, N., Won, K. J., Gonzalez-Hernandez, G., & Moore, J. H.
- Affiliations
- Cedars-Sinai Medical Center (US)
- University of Pennsylvania (US)
- Corresponding author
- Meyer, Jesse G.
- Moore, Jason H.
- ORCID
- Venue
- BioData Mining
- Published
- 2023-07-13
- Volume
- 16
- Issue
- 1
- Pages
- 20-20
- ISSN
- 1756-0381 (ISSN-L)
- PMC
- PMC10339472
- OpenAlex
- W4384200891
- Semantic Scholar
- e95b10b9a8415874f24275b665628fc340eee5dc
- Cited by
- 569
- References
- 23
- Citations by year
- 2026: 104 · 2025: 215 · 2024: 220 · 2023: 29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BioData Mining (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (AG066833)
- National Institute of General Medical Sciences (R35GM142502)
- National Institute of Allergy and Infectious Diseases (R01AI164481)
- U.S. National Library of Medicine (R01LM011176)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- large language models, ChatGPT, academic writing, artificial intelligence in education, AI-assisted programming, plagiarism, algorithmic bias, AI accuracy, research tools, ethics of AI use
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
