AI-assisted academic cheating: a conceptual model based on postgraduate student voices
Open accessAbstract
Introduction As AI tools become more widely used in higher education, concerns about AI-assisted academic cheating are growing. This study examines how postgraduate students interpret these behaviors. Methods We conducted an exploratory qualitative study. We analyzed ten ten course-embedded reflective essays using conventional content analysis and identified 159 meaning units, 34 codes, 12 categories, and 6 themes. Results Students described two main forms of AI-assisted cheating: misusing AI to complete academic tasks and improperly using AI-generated content. They attributed these behaviors to work pressure, ethical ambiguity, AI affordances, and gaps in institutional policies. They also proposed solutions, including clearer guidelines, improved assessment design, and stronger ethics education. Discussion The findings show that students construct their views on AI-assisted cheating within social, technological, and institutional contexts. Strengthening policy clarity and promoting a culture of ethical AI use can help institutions address these emerging challenges.
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
Cites
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?
Geographic diversity across Global North and South contexts strengthens external validity.
“Geographic diversity across Global North and South contexts strengthens external validity (Hanh and Duyen, 2025; Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Section 3.6 (Limitations; page unavailable) says the study drew on a single postgraduate cohort, limiting diversity, and that its findings should not be treated as generalizable. Section 3.2 (Research design; page unavailable) likewise says its aim was not to generate generalizable claims. This paper therefore does not provide the claimed geographic diversity or resulting external validity, though the other cited studies are not assessed here. (LLM, conf=0.84)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredInstitutional responses remain reactive and piecemeal, framing AI primarily as an integrity issue requiring policing rather than a pedagogical opportunity requiring curriculum redesign.
“Institutional responses remain reactive and piecemeal, framing AI primarily as an integrity issue requiring policing rather than a pedagogical opportunity requiring curriculum redesign (Gonsalves, 2025; Hanh and Duyen, 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Section 4.2.2 describes absent AI-use policies and limited faculty guidance, while section 5.2 describes a policy vacuum and implementation lag. Section 5.2 also calls for a shift from enforcement to ethical empowerment and discusses curricular integration and assessment redesign. The paper does not establish that existing institutional responses are primarily policing-oriented or piecemeal rather than pedagogical. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSubstantial evidence relies on convenience sampling and self-reported data, introducing potential social desirability bias regarding misconduct behaviors.
“but substantial evidence relies on convenience sampling and self-reported data introducing potential social desirability bias regarding misconduct behaviors (Amani and Bisriyah, 2025; Hanh and Duyen, 2025; Karkoulian et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Section 2.3 reports that students concealed AI-assisted cheating when asked directly and that anonymous methods produced higher admission rates, supporting the concern about social desirability bias. Sections 3.2 and 3.6 describe this study's data as reflective essays from one postgraduate cohort, but the paper does not establish that a substantial body of evidence relies on convenience sampling and self-reported misconduct behaviors. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredStudents operating under intense time constraints, managing multiple competing obligations, and facing high-stakes assessment systems make pragmatic decisions where immediate survival needs override abstract commitments to long-term skill development.
“Students operating under intense time constraints, managing multiple competing obligations, and facing high-stakes assessment systems make pragmatic decisions where immediate survival needs override abstract commitments to long-term skill development (Hanh and Duyen, 2025; Peterson, 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredUndergraduates prioritize efficiency and accessibility, while postgraduates express ethical ambiguity and concerns about voice erosion despite recognizing benefits for lexical diversity.
“Undergraduates prioritize efficiency and accessibility, while postgraduates express ethical ambiguity and concerns about voice erosion despite recognizing benefits for lexical diversity (Hanh and Duyen, 2025; Shi et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Section 5.1.1 reports ethical ambiguity among the postgraduate students studied. Section 5.1.2 says those students highlighted AI’s efficiency and accessibility; it does not establish that undergraduates prioritize them. The passages found do not report postgraduate concerns about voice erosion or benefits for lexical diversity, and the study does not make the claimed undergraduate–postgraduate comparison. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Ethical ambiguity and institutional policy voids enable rationalization of full task delegation as students develop situational interpretations without explicit guidance.
“Ethical ambiguity and institutional “policy voids” enable rationalization of full task delegation as students develop situational interpretations without explicit guidance (Hanh and Duyen, 2025; Kumar et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Sections 4.2.1 and 4.2.2 say that ethical ambiguity, unclear policies, and limited guidance lead students to construct their own rules and can make AI misuse appear acceptable. But section 4.1.1 says students viewed fully delegating academic tasks to AI as a clear violation; the paper does not establish that policy voids led them to rationalize that specific practice. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCurrent AI integration frameworks reveal significant gaps characterized by resource disparities, training deficits, and persistent policy voids.
“Current AI integration frameworks reveal significant gaps characterized by resource disparities, training deficits, and persistent “policy voids” (Hanh and Duyen, 2025; Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Section 4.2.2 identifies absent AI-use policies and limited faculty guidance; section 4.3.1 proposes instructor training. These support the policy-void point and, more indirectly, a training gap. The paper does not establish resource disparities as a gap in AI integration frameworks. (LLM, conf=0.86)
Experimental — accuracy not yet measuredInappropriate AI usage stems from socio-academic pressures including overloaded schedules, work pressure, and high-stakes grading systems.
“Inappropriate usage stems from socio-academic pressures including overloaded schedules, work pressure, and high-stakes grading systems (Hanh and Duyen, 2025; Peterson, 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
AI's technological affordances-speed, accessibility, and perceived low detection risk-create conditions where AI-giarism appears as a viable shortcut.
“AI’s technological affordances—speed, accessibility, and perceived low detection risk—create conditions where “AI-giarism” appears as a viable shortcut (Hanh and Duyen, 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Product-process assessment mitigates "full delegation" by rewarding the cognitive journey over the final output.
“Mitigates “full delegation” by rewarding the cognitive journey over the final output (Hanh and Duyen, 2025; Wang, 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Contextual pressures, institutional policy voids, and technological affordances converge to create "structural vulnerability" to AI misuse.
“Instead, a more nuanced picture emerges where contextual pressures, institutional policy voids, and technological affordances converge to create what might be termed “structural vulnerability” to AI misuse.”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Hanh and Duyen, 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Lave, J., & Wenger, E. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815355 · Cited by 33,219 · see citing works
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877 · see citing works
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
A hands-on guide to doing content analysis
Erlingsson, C. L., & Brysiewicz, P. (2017) · African Journal of Emergency Medicine · doi:10.1016/j.afjem.2017.08.001 · Cited by 2,377 · see citing works
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · see citing works
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 1,826 · see citing works
Cheating in Academic Institutions: A Decade of Research
McCabe, D. L., Trevino, L. K., & Butterfield, K. D. (2001) · Ethics & Behavior · doi:10.1207/s15327019eb1103_2 · Cited by 1,379 · see citing works
Academic Dishonesty: Honor Codes and other Contextual Influences
McCabe, D. L., & Trevino, L. K. (1993) · The Journal of Higher Education · doi:10.1080/00221546.1993.11778446 · Cited by 855 · see citing works
Impact of ChatGPT on learners in a L2 writing practicum: an exploratory investigation
Yan, D. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11742-4 · Cited by 713 · see citing works
Jeon, J., & Lee, S. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11834-1 · Cited by 696 · see citing works
Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions
Kooli, C. (2023) · Sustainability · doi:10.3390/su15075614 · Cited by 666 · see citing works
ChatGPT in education: Strategies for responsible implementation
Halaweh, M. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13036 · Cited by 607 · see citing works
Yusuf, A., Pervin, N., & Román-González, M. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00453-6 · Cited by 554 · see citing works
What ChatGPT means for universities: Perceptions of scholars and students
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.22 · Cited by 431 · see citing works
Academic integrity: A review of the literature
Macfarlane, B., Zhang, J., & Pun, A. (2014) · Studies in Higher Education · doi:10.1080/03075079.2012.709495 · Cited by 359 · see citing works
Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023) · European Journal of Engineering Education · doi:10.1080/03043797.2023.2213169 · Cited by 336 · see citing works
Sallam, M., Salim, N., Barakat, M., & Al-Tammemi, A. (2023) · Narra J · doi:10.52225/narra.v3i1.103 · Cited by 331 · see citing works
The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach
Mai, D. T. T., da, C. A. N., & Hanh, N. V. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1328769 · Cited by 310 · see citing works
Self-citationChaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023) · Cogent Education · doi:10.1080/2331186x.2023.2210461 · Cited by 262 · see citing works
Core elements of exemplary academic integrity policy in Australian higher education
Bretag, T., Mahmud, S., Wallace, M. C., Walker, R., James, C., Green, M., East, J., McGowan, U., & Patridge, L. (2011) · International Journal for Educational Integrity · doi:10.21913/ijei.v7i2.759 · Cited by 208 · see citing works
Challenges in addressing plagiarism in education
Bretag, T. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001574 · Cited by 164 · see citing works
Plagiarism, the Internet, and Student Learning
Sutherland‐Smith, W. (2008) · Routledge eBooks · doi:10.4324/9780203928370 · Cited by 162 · see citing works
Attitudes Toward, and Intentions to Report, Academic Cheating Among Students in Singapore
Lim, V. K. G., & See, S. K. B. (2001) · Ethics & Behavior · doi:10.1207/s15327019eb1103_5 · Cited by 142 · see citing works
Artificial intelligence: threat or asset to academic integrity? A bibliometric analysis
Rodrigues, M. M. M., Silva, R., Borges, A. P., Franco, M., & Oliveira, C. (2024) · Kybernetes · doi:10.1108/k-09-2023-1666 · Cited by 142 · see citing works
Nguyen, H. M., & Goto, D. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12495-4 · Cited by 102 · see citing works
Giannos, P., & Delardas, O. (2023) · JMIR Medical Education · doi:10.2196/47737 · Cited by 100 · see citing works
ChatGPT in Clinical Toxicology
Sabry Abdel-Messih, M., & Kamel Boulos, M. N. (2023) · JMIR Medical Education · doi:10.2196/46876 · Cited by 95 · see citing works
Can markers detect contract cheating? Results from a pilot study
Dawson, P., & Sutherland‐Smith, W. (2017) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1336746 · Cited by 93 · see citing works
Oravec, J. A. (2023) · The Journal of Interactive Learning Research · doi:10.70725/304731gmmvhw · Cited by 88 · see citing works
Embracing ChatGPT: Implications of Emergent Language Models for Academia and Libraries
Houston, A. B., & Corrado, E. M. (2023) · Technical Services Quarterly · doi:10.1080/07317131.2023.2187110 · Cited by 77 · see citing works
Evaluating the performance of ChatGPT in medical physiology university examination of phase I MBBS
Mahila, S., Jaleel, I., & Surapaneni, K. M. (2023) · AJP Advances in Physiology Education · doi:10.1152/advan.00036.2023 · Cited by 72 · see citing works
HEXACO, the Dark Triad, and Chat GPT: Who is willing to commit academic cheating?
Greitemeyer, T., & Kastenmüller, A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19909 · Cited by 39 · see citing works
Examining the relationship between student attitude and academic cheating
Yu, H., Glanzer, P. L., & Johnson, B. R. (2020) · Ethics & Behavior · doi:10.1080/10508422.2020.1817746 · Cited by 37 · see citing works
Is use of ChatGPT cheating? Students of health professions perceptions
Kazley, A. S., Andresen, C., Mund, A. R., Blankenship, C., & Segal, R. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2385667 · Cited by 26 · see citing works
Raskin, J. D., & Debany, A. E. (2017) · Journal of Constructivist Psychology · doi:10.1080/10720537.2017.1383954 · Cited by 20 · see citing works
Can, V.-D., & Nguyen, V. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2473851 · Cited by 16 · see citing works
Greitemeyer, T., & Kastenmüller, A. (2024) · Technology, Mind, and Behavior · doi:10.1037/tmb0000133 · Cited by 16 · see citing works
Preschool STEM Activities and Associated Outcomes
Hà, V. T. T., Hai, B. M., Mai, D. T. T., & Hanh, N. V. (2023) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v13i8.42177 · Cited by 15 · see citing works
Self-citationBenuyenah, V., & Dewnarain, S. (2024) · International Journal of Distance Education Technologies · doi:10.4018/ijdet.348061 · Cited by 12 · see citing works
Technological Mediation Theory and the Moral Suspension Problem
Liu, Z. (2022) · Human Studies · doi:10.1007/s10746-021-09617-z · Cited by 10 · see citing works
Mai, D. T. T., & Hanh, N. V. (2024) · International Journal of Computer-Assisted Language Learning and Teaching · doi:10.4018/ijcallt.349972 · Cited by 10 · see citing works
Self-citationAcademic Integrity in the Twenty-First Century: A Teaching and Learning Imperative (review)
Chesney, T. (2009) · Review of higher education/The review of higher education · doi:10.1353/rhe.0.0088 · Cited by 8 · see citing works
Pariyanti, E., Wibowo, M., Sultan, Z., & Siolemba Patiro, S. P. (2026) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-10-2024-0522 · Cited by 6 · see citing works
Technology Education in Vietnam
Hanh, N. V., & Turner, A. (2024) · Routledge eBooks · doi:10.4324/9781003298700-12 · Cited by 6 · see citing works
Self-citationAcademic Integrity of Undergraduates: The CETYS University Case
Lau, J., Esquivel, J. L. B., Barrios, D. J. S., & Rivera, A. G. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-99885-1_47 · Cited by 5 · see citing works
Models of rational agency in human-centered AI: the realist and constructivist alternatives
Sparks, J., & Wright, A. T. (2025) · AI and Ethics · doi:10.1007/s43681-025-00658-z · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Abd-Elaal, E.-S., Gamage, S. H., and Mills, J. E.. (2019). Artificial intelligence is a tool for cheating academic integrity. 30th Annual Conference for the Australasian Association for Engineering Education (AAEE 2019): Educators Becoming Agents of Change: Innovate, Integrate, Motivate, 397-403.
- Asmarita, D., Muyassaroh, I. K., and Saptaputra, I.. (2024). Causality of the artificial intelligence paradigm on constructivist perspective for student academic ethics. Proceeding International Conference on Religion, Science and Education, 3, 993-1004.
- Creswell, J. W., and Poth, C. N. (2016). Qualitative inquiry and research design: choosing among five approaches: 4th edition. Thousand Oaks, California, US: Sage publications.
- Verbeek, P.-P. (2005). What things do: Philosophical reflections on technology, agency, and design. Pennsylvania: Penn State Press.
Cited by
Sorted by citations
Oliveira, A. P., Coelho, M., Póvoa, O., Morgado, B., & Guerra, C. G. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1780142 · Cited by 11 · see citing works
da, C. A. N., & Nguyen, H. (2026) · International Journal of Teacher Education and Professional Development · doi:10.4018/ijtepd.410300 · Cited by 1 · see citing works
Horváth, L., Eger, L., Tomczyk, Ł., Szwejka, Ł., Egerová, D., Rohlíková, L., Kincl, T., Pospíšil, J. Z., Medeková, K., Mikulcová, P., et al. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100357 · Cited by 1 · see citing works
AI-Supported Flipped Classroom in Teaching Business Mathematics: A Classroom Action Research
Tarwiyah, T., Anggono, A., Naphatthalung, N., Saputra, A., & Irawan, A. (2026) · Edu Cendikia Jurnal Ilmiah Kependidikan · doi:10.47709/educendikia.v6i01.8254 · Cited by 1 · see citing works
Abdellatif, M. S., Alshehri, M. A., Lamouchi, A., Rahmath, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1809174
Learners’ and Instructors’ Evaluation on Plagiarism, Paraphrasing, and Summarizing in EFL Writing
Haticetül Kübra, E. R., & Gümüş, C. (2025) · Uluslararası Özel Amaçlar için İngilizce Dergisi · doi:10.70870/joinesp.1834695
DECONSTRUCTING ACADEMIC MISCONDUCT: THE MODERATING ROLE OF IT IN A DIAMOND OF FRAUD ANALYSIS
Sangka, K. B., Suryaningsih, K., & Pradianto, M. A. (2026) · JURNAL EDUSCIENCE · doi:10.36987/jes.v13i1.7667
Cummins, L. K., Bedford, L., & Crowe, D. (2026) · Journal of Online Graduate Education · doi:10.65201/001c.161741
The Collective Engagement Framework
Eamer, A., & Gibson, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5398-2.ch005
A survey on the use of generative artificial intelligence by postgraduate students in ChinaPreprint
Qi, R., Fan, C., Wang, J., & Hu, W. (2026) · Research Square · doi:10.21203/rs.3.rs-9862747/v1
The Ethical Use of AI Technology Applications Among University Students in Pahang
Mohamed, M. R., Anas, N., Yaacob, Z., Giran, A. H. A., Lot, N., Mokhtar, A. E., & Ghazali, Z. M. (2026) · Asia Pacific Journal of Educators and Education · doi:10.21315/apjee2026.41.1.12
From Syntax Trees to Embeddings: A Comparative Study of AI-Generated Code Detection
Jakimoski, M., Toshevska, M., & Gievska, S. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591667
da, C. A. N., Duong, M. T. T., Nguyen, H., & Nguyen, C. H. (2026) · Lecture notes in educational technology · doi:10.1007/978-981-92-1742-7_9
Nguyen, H. V., & Duong, M. T. T. (2026) · Trends in Higher Education · doi:10.3390/higheredu5030083
Mutebi, J., Brian, M., ADABARA, I., & Muhammad, T. (2026) · F1000Research · doi:10.12688/f1000research.179680.1
Reshaping the doctoral journey in the AI era
Le, C. X., & Shih, W. (2026) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v15i5.40091
Beyond AI Use: Understanding University Students’ Acceptance of AI-Assisted Academic Misuse
Alzougool, B., & Banna, S. A. (2026) · European Conference on e-Learning · doi:10.34190/ecel.25.1.5340
Kırcaburun, K., Emirtekin, E., & Balta, S. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14110277
- Authors
- Hanh, N. V., & Duyen, N. T.
- Affiliations
- Trường Đại học Khoa học và Công nghệ Hà Nội (VN)
- Corresponding author
- Hanh, Nguyen Van
- ORCID
- Duyen, Nguyen Thi
- Venue
- Frontiers in Computer Science
- Publisher
- Frontiers Media SA
- Published
- 2025-11-26
- Volume
- 7
- ISSN
- 2624-9898 (ISSN-L)
- OpenAlex
- W4416729346
- Cited by
- 12
- References
- 45
- Citations by year
- 2026: 11 · 2025: 1
- FWCI
- 12.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Computer Science (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Trường Đại học Bách Khoa Hà Nội
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI in higher education, graduate students, student perceptions, AI affordances, academic integrity, moral ambiguity, institutional policy, assessment design, ethics education, occupational stress
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
