The DOI matches its Crossref record.
‘Words Are Flowing Out Like Endless Rain Into a Paper Cup’: ChatGPT & Law School Assessments
Abstract
ChatGPT is a sophisticated large-language model able to answer high-level questions in a way that is undetectable by conventional plagiarism detectors. Concerns have been raised it poses a significant risk of academic dishonesty in ‘take-home’ assessments in higher education. To evaluate this risk in the context of legal education, this project had ChatGPT generate answers to twenty-four different exams from an English-language law school based in a common law jurisdiction. It found that the system performed best on exams that were essay-based and asked students to discuss international legal instruments or general legal principles not necessarily specific to any jurisdiction. It performed worst on exams that featured problem-style or “issue spotting” questions asking students to apply an invented factual scenario to local legislation or jurisprudence. While the project suggests that for the most part conventional law school assessments are for the time being relatively immune from the threat ChatGPT brings, this is unlikely to remain the case as the technology advances. However, rather than attempt to block students from using AI as part of learning and assessment, this paper instead proposes three ways students may be taught to use it in appropriate and ethical ways. While it is clear that ChatGPT and similar AI technologies will change how universities teach and assess (across disciplines), a solution of prevention or denial is no solution at all.
Overview
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
Verification
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?
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
“Traditional assessment methods, such as multiple-choice exams, standardised essays, practical assignments, and presentations, are increasingly inadequate in evaluating student learning in AI-infused environments [ 32, 33 ].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [33]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 5
- Jonah Siekmann et al., 'Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning', Computer Science: Robotics (Research paper, 18 May 2021) <https://arxiv.org/abs/2105.08328>. 85 Infra, 24. 103 Rebecca Awdry and Philip M Newton, 'Staff Views on Commercial Contract Cheating in Higher Education: A Survey Study in Australia and the UK' (2009) 78 Higher Education 593, 594. 104 See eg Sarah Elaine Eaton, 'Contract Cheating in Canada: A Comprehensive Overview' in Sarah Elaine Eaton and Julia Christensen Hughes (eds), Academic Integrity in Canada (Springer, 2022); Shawren Singh and Dan Remenyi, 'Plagiarism and Ghostwriting: The Rise in Academic Misconduct' (2016) 112(5-6) South African Journal of Science. 105 In 2015 Australian media revealed the widespread use of cheating services advertised at some of the country's top universities, kick-starting public consternation with the practice: Amy McNeilage & Lisa Visentin, 'Students Enlist MyMaster Website to Write Essays, Assignments', Sydney Morning Herald (online, 12 November 2014) <https://www.smh.com.au/education/students-enlist-mymaster-website-to-write- essays-assignments-20141110-11k0xg.html>. 106 · arXiv
- Shiva Sivasubramaniam, Kalliopi Kostelidou, and Sharavan Ramachandran, 'A Close Encounter With Ghost-Writers: An Initial Exploration Study on Background, Strategies and Attitudes of Independent Essay Providers' (2016) 12(1) International Journal for Educational Integrity 1. 108 See eg Rowena Harper et al., 'Contract Cheating: A Survey of Australian University Teaching Staff' (2018) 1 Legal Education Review 1; Tracey Bretag et al., 'Contract Cheating and Assessment Design: Exploring the Relationship' (2019) 44(5) Assessment & Evaluation in Higher Education 676. 109 Tertiary Education Quality and Standards Agency Amendment (Prohibiting Academic Cheating Services) Bill 2019 (Cth) <https://parlinfo.aph.gov.au/parlInfo/download/legislation/bills/r6483_aspassed/toc _pdf/19257b01.pdf>. 110 As of October 2022, TEQSA had successfully applied to block 150 websites targeting Australian students and it said it was working through a 'priority list' of 580. See 'TEQSA Disrupts Access to Another 110 Illegal Academic Cheating Websites', TEQSA (Blogpost, 13 October 2022) <https://www.teqsa.gov.au/about-us/news-and- events/latest-news/teqsa-disrupts-access-another-110-illegal-academic-cheating- websites>. 111 Qualifications and Quality Assurance (Education and Training) (Amendment) Act 2019 <https://www.irishstatutebook.ie/eli/2019/act/32/enacted/en/html>. 112 Skills and Post-16 Education Act 2022 <https://www.legislation.gov.uk/ukpga/2022/21/enacted>. 120 See eg Elliott S. Milstein, 'Clinical Legal Education in the United States: In-House Clinics, Externships, and Simulations' (2001) 51(3) Journal of Legal Education 375;
- Neil J. Dilloff, 'Law School Training: Bridging the Gap between Legal Education and the Practice of Law' (2013) 24(2) Stanford Law & Policy Review 425;
- Mark Spiegel, 'Theory and Practice in Legal Education: An Essay on Clinical Education' (1987) 34(3) UCLA Law Review 577;
- Margaret Martin Barry, Jon C. Dubin, and Peter A. Joy, 'Clinical Education for This Millennium: The Third Wave' (2000) 7(1) Clinical Law Review 1. 121 See eg Ian Weinstein, 'Testing Multiple Intelligences: Comparing Evaluation by Simulation and Written Exam' (2001) 8(1) Clinical Law Review 247;
Cited by
Sorted by citations
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023) · BenchCouncil Transactions on Benchmarks, Standards and Evaluations · doi:10.1016/j.tbench.2023.100115 · Cited by 535
Mohamed, A. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-11917-z · Cited by 387
Ibrahim, H., Liu, F., Asim, R., Battu, B., Benabderrahmane, S., Alhafni, B., Adnan, W., Alhanai, T., AlShebli, B., Baghdadi, R., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38964-3 · Cited by 154
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
Malik, A., Khan, M. L., Hussain, K., Qadir, J., & Tarhini, A. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2024.2409407 · Cited by 57
Ncube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31
Metadata
- Authors
- Hargreaves, S.
- ORCID
- Hargreaves, Stuart
- Publisher
- Elsevier BV
- Published
- 2023
- Cited by
- 30
- References
- 0
- Type
- Preprint
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