Cited by 356Academic integrity and plagiarismLaw, AI, and Intellectual PropertyEthics and Social Impacts of AI

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3)

Open access

Dehouche, N·2021·Ethics in Science and Environmental Politics

Open this paper in CoChat — free account Open PDF

Abstract

As if 2020 was not a peculiar enough year, its fifth month saw the relatively quiet publication of a preprint describing the most powerful natural language processing (NLP) system to date—GPT-3 (Generative Pre-trained Transformer-3)—created by the Silicon Valley research firm OpenAI. Though the software implementation of GPT-3 is still in its initial beta release phase, and its full capabilities are still unknown as of the time of this writing, it has been shown that this artificial intelligence can comprehend prompts in natural language, on virtually any topic, and generate relevant original text content that is indistinguishable from human writing. Moreover, access to these capabilities, in a limited yet worrisome enough extent, is available to the general public. This paper presents examples of original content generated by the author using GPT-3. These examples illustrate some of the capabilities of GPT-3 in comprehending prompts in natural language and generating convincing content in response. I use these examples to raise specific fundamental questions pertaining to the intellectual property of this content and the potential use of GPT-3 to facilitate plagiarism. The goal is to instigate a sense of urgency, as well as a sense of present tardiness on the part of the academic community in addressing these questions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.3354/esep00195

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587
Pre-trained models for natural language processing: A survey
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1647-3 · Cited by 1,591
On Certain Formal Properties of Grammars
Chomsky, N. (1959) · Information and Control · doi:10.1016/s0019-9958(59)90362-6 · Cited by 1,513
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043
Moral Machines
Wallach, W., & Allen, C. (2009) · Oxford University Press · doi:10.1093/acprof:oso/9780195374049.001.0001 · Cited by 711 · cited 1×
“It is seen as a major extinction risk by Bostrom (2016), and ethics discourse on it has mainly focused on the potential for an intrinsic morality in autonomous systems possessing this form of intelligence (Wallach & Allen 2009).”
Background
Autonomous Vehicle Technology: A Guide for Policymakers
Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2016) · doi:10.7249/rr443-2 · Cited by 653
Viewpoint: when will AI exceed human performance? evidence from AI experts
Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11222 · Cited by 579
Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity: A Review and Research Agenda
Wang, W., & Siau, K. (2019) · Journal of Database Management · doi:10.4018/jdm.2019010104 · Cited by 452
Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry
Köbis, N., & Mossink, L. D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106553 · Cited by 406

Show all 25 references

Research works printed but not linked: 6

Asaro PM (2016) The liability problem for autonomous artificial agents. Proc AAAI Spring Symposium Series, Ethical and Moral Considerations in Non-Human Agents track. p 190-194. https://www.aaai.org/ocs/index.php/SSS/SSS16/paper/view/12699
Bostrom N (2016) Superintelligence. Oxford University Press, Oxford
Francke E, Alexander B (2019) The potential influence of artificial intelligence on plagiarism a higher education perspective. In: Griffiths P, Kabir MN (eds) Proc European Conference on the Impact of Artificial Intelligence and Robotics. EM Normandie Business School, Oxford, p 131-140
Govindarajulu NS, Bringsjord S (2015) Ethical regulation of robots must be embedded in their operating systems. In: Trappl R (ed) A construction manual for robots’ ethical systems. Springer, Berlin, Heidelberg, p 85-99
Miyashita K, Russell D (1995) Keiretsu: inside the hidden Japanese conglomerates. McGraw-Hill, New York, NY
10.24318/cope2018.1.3 · DOI

Sorted by citations

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
“It is becoming increasingly difficult to distinguish whether a text is machine-or human-generated, presenting an additional major challenge to teachers and educators [51, 52, 53, 54].”
Background
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
Chatting 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
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806
The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review
Zhai, C., Wibowo, S., & Li, L. D. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00316-7 · Cited by 1,567
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080
“This poses a challenge to educators and researchers [57,60–62].”
Background
ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919
Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond
Perkins, M. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.02.07 · Cited by 839
GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions
Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3389497 · Cited by 907
The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research
Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S., et al. (2023) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2023.05.016 · Cited by 565
Not quite eye to A.I.: student and teacher perspectives on the use of generative artificial intelligence in the writing process
Barrett, A., & Pack, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00427-0 · Cited by 391
Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation
Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569759 · Cited by 401
The effects of artificial intelligence applications in educational settings: Challenges and strategies
Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123076 · Cited by 317
On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?
Malinka, K., Peresíni, M., Firc, A., Hujnák, O., & Janus, F. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588827 · Cited by 287
“In his opinion paper on GPT-3 [5], Dehouche shows it can generate academic papers which pass current plagiarism checks, while Thunström and Steingrimsson [7] used it to generate a full scientific paper ( however, it contains typical flaws such as weak claims and unfit references).”
Background
“It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers
Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3617367 · Cited by 256
“We expect significant implications ahead for issues of academic integrity, and a clear need for an updated definition of plagiarism [30].”
Background
AI model GPT-3 (dis)informs us better than humans
Spitale, G., Biller-Andorno, N., & Germani, F. (2023) · Science Advances · doi:10.1126/sciadv.adh1850 · Cited by 241
ChatGPT: Vision and challenges
Gill, S. S., & Kaur, R. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.05.004 · Cited by 294
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196
Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3
Haluza, D., & Jungwirth, D. (2023) · Systems · doi:10.3390/systems11030120 · Cited by 195
The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors
Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186
Artificial Intelligence and Public Health: An Exploratory Study
Jungwirth, D., & Haluza, D. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20054541 · Cited by 189
Sparks: Inspiration for Science Writing using Language Models
Gero, K. I., Liu, V., & Chilton, L. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533533 · Cited by 161
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · doi:10.20944/preprints202303.0473.v1 · Cited by 1,080
GPT-3 and InstructGPT: technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry
Chan, A. (2023) · AI and Ethics · doi:10.1007/s43681-022-00148-6 · Cited by 155
ChatGPT in higher education - a synthesis of the literature and a future research agenda
Bhullar, P. S., Joshi, M., & Chugh, R. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12723-x · Cited by 139
Students’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct
Chan, C. K. Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13151-7 · Cited by 128
Teachers, Parents, and Students' perspectives on Integrating Generative AI into Elementary Literacy Education
Han, A., Zhou, X., Cai, Z., Han, S., Ko, R., Corrigan, S., & Peppler, K. A. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642438 · Cited by 114
“Consequently, there is a need for further research into writers’ ownership, authorship, and plagiarism, in addition to developing new methods for assessing and measuring writers’ progress [29, 58, 59].”
On the creativity of large language models
Franceschelli, G., & Musolesi, M. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02127-3 · Cited by 113
Exploring the role of ChatGPT in higher education: opportunities, challenges and ethical considerations
Zeb, A., Ullah, R., & Karim, R. (2024) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-04-2023-0046 · Cited by 123
Mastering knowledge: the impact of generative AI on student learning outcomes
Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2026) · Studies in Higher Education · doi:10.1080/03075079.2025.2487570 · Cited by 141
Algorithmically-driven writing and academic integrity: exploring educators' practices, perceptions, and policies in AI era
Gustilo, L., Ong, E., & Lapinid, M. R. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-024-00153-8 · Cited by 89
Harnessing GPT-4 for generation of cybersecurity GRC policies: A focus on ransomware attack mitigation
McIntosh, T. R., Liu, T., Sušnjak, T., Alavizadeh, H., Ng, A., Nowrozy, R., & Watters, P. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103424 · Cited by 93
ChatGPT: The brightest student in the class
Cano, E. V., Ramírez‐Hurtado, J. M., López, J. M. S., & Menéses, E. L. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101380 · Cited by 105
Rethinking Plagiarism in the Era of Generative AI
Hutson, J. (2024) · Journal of Intelligent Communication · doi:10.54963/jic.v4i1.220 · Cited by 83
Navigating LLM ethics: advancements, challenges, and future directions
Jiao, J., Afroogh, S., Xu, Y., & Phillips, C. (2025) · AI and Ethics · doi:10.1007/s43681-025-00814-5 · Cited by 171
AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
Wu, C., Zhang, H., & Carroll, J. M. (2024) · Future Internet · doi:10.3390/fi16100354 · Cited by 99
“The fact that AI is trained with existing work and its ability to quickly generate content that may be unauthorized imitations impose fundamental challenges to the regulation of plagiarism and academic integrity (Dehouche 2021; Smits and Borghuis 2022).”
How do students use ChatGPT as a writing support?
Levine, S., Beck, S. W., Mah, C., Phalen, L., & PIttman, J. (2025) · Journal of Adolescent & Adult Literacy · doi:10.1002/jaal.1373 · Cited by 83
ChatGPT: deconstructing the debate and moving it forward
Coeckelbergh, M., & Gunkel, D. J. (2023) · AI & Society · doi:10.1007/s00146-023-01710-4 · Cited by 88
The knowledge and innovation challenges of ChatGPT: A scoping review
Ali, O., Murray, P., Momin, M., & Al–Anzi, F. S. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102402 · Cited by 84
How should the advancement of large language models affect the practice of science?
Binz, M., Alaniz, S., Roskies, A., Aczel, B., Bergstrom, C. T., Allen, C., Schad, D., Wulff, D., West, J. D., Zhang, Q., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401227121 · Cited by 74
Advancing Students’ Academic Excellence in Distance Education: Exploring the Potential of Generative AI Integration to Improve Academic Writing Skills
Maphoto, K. B., Sevnarayan, K., Mohale, N. E., Suliman, Z., Ntsopi, T. J., & Mokoena, D. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.649 · Cited by 129
Exploring the Ethical Dimensions of Using ChatGPT in Language Learning and Beyond
Vaccino‐Salvadore, S. (2023) · Languages · doi:10.3390/languages8030191 · Cited by 110
Demystifying ChatGPT: An In-depth Survey of OpenAI’s Robust Large Language Models
Bhattacharya, P., Prasad, V. K., Verma, A., Gupta, D. K., Sapsomboon, A., Viriyasitavat, W., & Dhiman, G. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10115-5 · Cited by 84
A Constructivist Approach to Integrating AI in Marketing Education: Bridging Theory and Practice
Richter, S., Giroux, M., Piven, I. P., Sima, H., & Dodd, P. (2024) · Journal of Marketing Education · doi:10.1177/02734753241288876 · Cited by 95
A Review of AI-Powered Writing Tools and Their Implications for Academic Integrity in the Language Classroom
Roe, J., Renandya, W. A., & Jacobs, G. M. (2023) · Journal of English and Applied Linguistics · doi:10.59588/2961-3094.1035 · Cited by 116
Can ChatGPT be an author? Generative AI creative writing assistance and perceptions of authorship, creatorship, responsibility, and disclosure
Formosa, P., Bankins, S., Matulionytė, R., & Ghasemi, O. (2024) · AI & Society · doi:10.1007/s00146-024-02081-0 · Cited by 98
Crafting a Museum Guide Using ChatGPT4
Trichopoulos, G., Konstantakis, M., Caridakis, G., Katifori, A., & Koukouli, M. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7030148 · Cited by 66
Higher Education Faculty Perceptions of ChatGPT and the Influencing Factors: A Sentiment Analysis of X
Mamo, Y., Crompton, H., Burke, D., & Nickel, C. (2024) · TechTrends · doi:10.1007/s11528-024-00954-1 · Cited by 65
Distinguishing Human-Written and ChatGPT-Generated Text Using Machine Learning
Alamleh, H., AlQahtani, A. A. S., & ElSaid, A. A. (2023) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds58326.2023.10137767 · Cited by 70
The Benefits and Limitations of ChatGPT in Business Education and Research: A Focus on Management Science, Operations Management and Data AnalyticsPreprint
Cribben, I., & Zeinali, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4404276 · Cited by 70
ChatGPT and higher education assessments: More opportunities than concerns?
Gamage, K. A. A., Dehideniya, S. C. P., Xu, Z., & Tang, X. (2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.32 · Cited by 84
Navigating Generative AI (ChatGPT) in Higher Education: Opportunities and Challenges
Wang, T. (2023) · Lecture Notes in Educational Technology · doi:10.1007/978-981-99-5961-7_28 · Cited by 56
Hey ChatGPT, give me a title for a paper about degree apathy and student use of AI for assignment writing
Playfoot, D. R., Quigley, M., & Thomas, A. G. (2024) · The Internet and Higher Education · doi:10.1016/j.iheduc.2024.100950 · Cited by 75
ChatGPT in Plastic and Reconstructive Surgery
Sharma, S. C., Ramchandani, J. P., Thakker, A., & Lahiri, A. (2023) · Indian Journal of Plastic Surgery · doi:10.1055/s-0043-1771514 · Cited by 54
Speculative Propositions for Digital Writing Under the New Autonomous Model of Literacy
Robinson, B. (2022) · Postdigital Science and Education · doi:10.1007/s42438-022-00358-5 · Cited by 58
ChatGPT in students’ research: to forbid or to teach?
Sysoyev, P. V., & Filatov, E. M. (2023) · Вестник Тамбовского университета. Серия: Гуманитарные науки · doi:10.20310/1810-0201-2023-28-2-276-301 · Cited by 51
Health Care Trainees’ and Professionals’ Perceptions of ChatGPT in Improving Medical Knowledge Training: Rapid Survey Study
Hu, J.-M., Liu, F.-C., Chu, C.-M., & Chang, Y.-T. (2023) · Journal of Medical Internet Research · doi:10.2196/49385 · Cited by 50
How much can we rely on artificial intelligence chatbots such as the ChatGPT software program to assist with scientific writing?
Dashti, M., Londono, J., Ghasemi, S., & Moghaddasi, N. (2023) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2023.05.023 · Cited by 53
Seven HCI Grand Challenges Revisited: Five-Year Progress
Stephanidis, C., Salvendy, G., Antona, M., Duffy, V. G., Gao, Q., Karwowski, W., Konomi, S., Nah, F. F., Ntoa, S., Rau, P. P., et al. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2450411 · Cited by 59
Barriers to and Opportunities for the Adoption of Generative Artificial Intelligence in Higher Education in the Global South: Insights from Sri Lanka
Henadirage, A., & Gunarathne, N. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00439-5 · Cited by 48
Foundation Model-Based Multimodal Remote Sensing Data Classification
He, X. J., Chen, Y., Huang, L., Hong, D., & Du, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3344698 · Cited by 55
ChatGPT in English Language Learning: Exploring Perceptions and Promoting Autonomy in a University EFL Context
Horn, K. R. V. (2024) · Teaching English as a Second or Foreign Language--TESL-EJ · doi:10.55593/ej.28109a8 · Cited by 89
DIGITAL DOPING FOR HISTORIANS: CAN HISTORY, MEMORY, AND HISTORICAL THEORY BE RENDERED ARTIFICIALLY INTELLIGENT?
Kansteiner, W. (2022) · History and Theory · doi:10.1111/hith.12282 · Cited by 46
Academic Writing with GPT-3.5 (ChatGPT): Reflections on Practices, Efficacy and Transparency
Buruk, O. '. (2023) · 26th International Academic Mindtrek Conference · doi:10.1145/3616961.3616992 · Cited by 46
Large Language Models for Cultural Heritage
Trichopoulos, G. (2023) · International Conference of the ACM Greek SIGCHI Chapter (CHIGreece) · doi:10.1145/3609987.3610018 · Cited by 45
Utilization of Writing Assistance Tools in Research in Selected Higher Learning Institutions in the Philippines: A Text Mining Analysis
Santiago, C. S., Embang, S. I., Acanto, R. B., Ambojia, K. W. P., Aperocho, M. D. B., Balilo, B. B., Cahapin, E. L., Conlu, M. T. N., Lausa, S. M., Laput, E. Y., et al. (2023) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.22.11.14 · Cited by 61
Exploring the opportunities and challenges of ChatGPT in academia
Islam, I., & Islam, M. N. (2024) · Discover Education · doi:10.1007/s44217-024-00114-w · Cited by 113
Strengths, Weaknesses, Opportunities, and Threats of Using ChatGPT in Scientific Research
Giray, L., Jacob, J., & Gumalin, D. L. (2024) · International Journal of Technology in Education · doi:10.46328/ijte.618 · Cited by 67
Pressure to Plagiarize and the Choice to Cheat: Toward a Pragmatic Reframing of the Ethics of Academic Integrity
McIntire, A., Calvert, I., & Ashcraft, J. (2024) · Education Sciences · doi:10.3390/educsci14030244 · Cited by 62
Comparing ChatGPT's correction and feedback comments with that of educators in the context of primary students' short essays written in English and Greek
Fokides, E., & Peristeraki, E. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12912-8 · Cited by 59
Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT
Mah, C., Walker, H., Phalen, L., Levine, S. R., Beck, S. W., & Pittman, J. (2024) · Education Sciences · doi:10.3390/educsci14050500 · Cited by 59
Fostering social-emotional learning through human-centered use of generative AI in business research education: an insider case study
Aure, P. A., & Cuenca, O. (2024) · Journal of Research in Innovative Teaching & Learning · doi:10.1108/jrit-03-2024-0076 · Cited by 42
Enhancing students’ learning experience in mathematics class through ChatGPT
Gouia‐Zarrad, R., & Gunn, C. L. (2024) · International Electronic Journal of Mathematics Education · doi:10.29333/iejme/14614 · Cited by 71
Can human intelligence safeguard against artificial intelligence? Exploring individual differences in the discernment of human from AI texts
Chein, J., Martinez, S., & Barone, A. (2024) · doi:10.21203/rs.3.rs-4277893/v1 · Cited by 38
Who Wrote This?: Detecting Artificial Intelligence–Generated Text from Human-Written Text
Kumar, R., & Mindzak, M. (2024) · Canadian Perspectives on Academic Integrity · doi:10.55016/ojs/cpai.v7i1.77675 · Cited by 38
Human intelligence can safeguard against artificial intelligence: individual differences in the discernment of human from AI texts
Chein, J. M., Martinez, S. A., & Barone, A. R. (2024) · Scientific Reports · doi:10.1038/s41598-024-76218-y · Cited by 38
Amazement and Trepidation: Implications of AI-Based Natural Language Production for the Teaching of Writing
Anson, C. M., & Straume, I. S. (2022) · Journal of Academic Writing · doi:10.18552/joaw.v12i1.820 · Cited by 42
How Large Language Models are Transforming Machine-Paraphrase Plagiarism
Wahle, J. P., Ruas, T., Kirstein, F., & Gipp, B. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.62 · Cited by 35
Postgraduate Students’ Perceptions on the Benefits Associated with Artificial Intelligence Tools on Academic Success: In Case of ChatGPT AI tool
Chauke, T. A., Mkhize, T. R., Methi, L. M., & Dlamini, N. P. (2024) · Journal of Curriculum Studies Research · doi:10.46303/jcsr.2024.4 · Cited by 43
Chinese University Students’ Attitudes and Perceptions in Learning English Using ChatGPT
Liu, B. (2023) · International Journal of Education and Humanities · doi:10.58557/(ijeh).v3i2.145 · Cited by 53
Developing a Curated Chatbot as an Exploratory Communication Tool for Chemistry Learning
Lolinco, A. T., & Holme, T. A. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00520 · Cited by 41
Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment
Marmolejo‐Ramos, F., Marrone, R. L., Korolkiewicz, M. W., Gabriel, F. C., Siemens, G., Joksimovíc, S., Yamada, Y., Mori, Y., Rahwan, T., Sahakyan, M., et al. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1465605 · Cited by 44
Generative Artificial Intelligence to Enhance Architecture Education to Develop Digital Literacy and Holistic Competency
Kee, T., Kuys, B., & King, R. B. (2024) · Journal of Artificial Intelligence in Architecture · doi:10.24002/jarina.v3i1.8347 · Cited by 46
AI ChatBots’ solutions to mathematical problems in interactive e-textbooks: Affordances and constraints from the eyes of students and teachers
Ergene, Ö., & ERGENE, B. Ç. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13121-z · Cited by 40
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
Wahle, J. P., Ruas, T., Meuschke, N., & Gipp, B. (2021) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl52503.2021.00065 · Cited by 35
A hybrid framework for creating artificial intelligence-augmented systematic literature reviews
Malik, F. S., & Terzidis, O. (2026) · Management Review Quarterly · doi:10.1007/s11301-025-00522-8 · Cited by 28
Exploring the potential of large language models and generative artificial intelligence (GPT): Applications in Library and Information Science
Formánek, M. (2024) · Journal of Library and Information Sciences · doi:10.1177/09610006241241066 · Cited by 33
Reacting to Generative AI: Insights from Student and Faculty Discussions on Reddit
Wu, C., Wang, X., Carroll, J. M., & Rajtmajer, S. M. (2024) · ACM Web Science Conference (WebSci) · doi:10.1145/3614419.3644014 · Cited by 31
A cross-national assessment of artificial intelligence (AI) Chatbot user perceptions in collegiate physics education
Agyare, B. O., Asare, J., Kraishan, A., Nkrumah, I., & Adjekum, D. K. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100365 · Cited by 46
Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?
Voß, S. (2023) · Sustainability · doi:10.3390/su15129625 · Cited by 25
Navigating the Transformative Impact of Artificial Intelligence on English Language Teaching: Exploring Challenges and Opportunities
Idham, A. Z., Rauf, W., & Rajab, A. (2024) · Jurnal Edukasi Saintifik · doi:10.56185/jes.v4i1.620 · Cited by 22
ChatGPT and imaginaries of the future of education: insights of Finnish teacher educators
Vartiainen, H., Valtonen, T., Kahila, J., & Tedre, M. (2024) · Information and Learning Sciences · doi:10.1108/ils-10-2023-0146 · Cited by 34
Applications and Implication of Generative AI in Non-STEM Disciplines in Higher Education
Wu, T., & Zhang, S. H. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-7587-7_29 · Cited by 27
An Empirical Study of AI Generated Text Detection Tools
Akram, A. (2023) · arXiv · doi:10.48550/arxiv.2310.01423 · Cited by 22
Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals
Yin, S., Huang, S., Xue, P., Xu, Z., Lian, Z., Ye, C., Ma, S., Liu, M., Hu, Y., Lu, P., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-03899-1 · Cited by 21
Pemanfaatan Artificial Intelegent (AI) Dalam Dunia Pendidikan
Diantama, S. (2023) · DEWANTECH Jurnal Teknologi Pendidikan · doi:10.61434/dewantech.v1i1.8 · Cited by 44
Critical Thinking and Problem-Solving in the Age of ChatGPT: Experiential-Bibliotherapy-Blogging Project
Sharma, D. (2024) · Business and Professional Communication Quarterly · doi:10.1177/23294906241254780 · Cited by 29
Can AI replace humans? Comparing the capabilities of AI tools and human performance in a business management education scenario
Herath, D. B., Ode, E., & Herath, G. B. (2025) · British Educational Research Journal · doi:10.1002/berj.4111 · Cited by 29
Impact of digital media literacy on attitude toward generative AI acceptance in higher education
Sergeeva, O. V., Masalimova, A. R., ZHELTUKHINA, M. R., Сергеевна, Ч. Л., Lutskovskaia, L., & Luzin, A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1563148 · Cited by 37

Authors: Dehouche, N.
Affiliations: Mahidol University (TH)
Corresponding author: Dehouche, N
ORCID: Dehouche, N
Venue: Ethics in Science and Environmental Politics
Publisher: Inter-Research Science Center
Published: 2021-03-25
Volume: 21
Pages: 17-23
ISSN: 1611-8014 (ISSN-L)
1863-5415
DOI: 10.3354/esep00195
OpenAlex: W3125358881
Cited by: 296
References: 25
Citations by year: 2026: 24 · 2025: 67 · 2024: 87 · 2023: 98 · 2022: 12 · 2021: 8
FWCI: 64.07
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: public-domain
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Ethics in Science and Environmental Politics (publishedVersion, public-domain)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
PhilPapers (PhilPapers Foundation) (submittedVersion)
Funders: Mahidol University International College
University of Oxford
Mahidol University
Région Normandie
Topics: Social Sciences › Education › Academic integrity and plagiarism
Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords: plagiarism, GPT-3, generative pre-trained transformer, large language models, intellectual property, academic integrity, AI-generated text

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.

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

Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3)

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks