‘Words are Flowing Out Like Endless Rain Into a Paper Cup’: ChatGPT & Law School Assessments
Open accessAbstract
ChatGPT is a sophisticated large-language model able to answer high-level questions in a way that is does not trigger conventional plagiarism detectors. Concerns have been raised that and similar forms of ‘generative AI’ pose a significant threat to academic integrity in higher education. To evaluate this risk in the context of legal education specifically, this project had ChatGPT (using the GPT3.5 model available in January 2023) 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 generative AI brings, the project provides only a baseline snapshot of how large-language models tackle assessment in higher education. As both the technology improves and students learn how to harness it, increasingly fewer forms of assessment will be beyond its reach. 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 generative AI will change how universities teach and assess (across disciplines), a solution of prevention or denial is no solution at all.
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GenAI models are trained on vast datasets scraped from the internet, often including copyrighted works, without explicit notification or consent from original creators.
“GenAI models are trained on vast datasets scraped from the internet, often including copyrighted works, without explicit notification or consent from original creators.”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Hargreaves, 2023
Section II, “The Basics of Generative AI & ChatGPT” (page unavailable), describes GPT-3’s vast web-derived training data and reports an objection to OpenAI’s use of scraped data without contributors’ consent. It also notes that one book dataset was not entirely copyright-free, but treats the possible contents of another as speculation. The paper does not establish that GenAI models generally often include copyrighted works or that creators were not explicitly notified. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThis has led to significant lawsuits alleging copyright infringement.
“This has led to significant lawsuits alleging copyright”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Hargreaves, 2023
Section II, “The Basics of Generative AI & ChatGPT” (page unavailable), mentions possible copyright-law violations arising from training data, but does not report lawsuits alleging copyright infringement. Searches for copyright lawsuits, litigation, and infringement claims found no passage addressing the asserted lawsuits. (LLM, conf=0.72)
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Contract cheating and assessment design: exploring the relationship
Bretag, T., Harper, R., Burton, M., Ellis, C., Newton, P., van Haeringen, K., Saddiqui, S., & Rozenberg, P. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2018.1527892 · Cited by 201 · see citing works
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Research works printed but not linked: 4
- 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
- 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;
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Kangwa, D., Msafiri, M. M., & Fute, A. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70109 · Cited by 22 · see citing works
Barus, O., Hidayanto, A. N., & Eitiveni, I. (2025) · Polyglot: Jurnal Ilmiah · doi:10.19166/pji.v21i2.10020 · Cited by 6 · see citing works
- Authors
- Hargreaves, S.
- Affiliations
- Chinese University of Hong Kong (HK)
- Corresponding author
- Hargreaves, Stuart
- ORCID
- Hargreaves, Stuart
- Venue
- Legal Education Review
- Publisher
- Bond University
- Published
- 2023-07-04
- Volume
- 33
- Issue
- 1
- ISSN
- 1033-2839 (ISSN-L)
- 1839-3713
- OpenAlex
- W4383104257
- Cited by
- 31
- References
- 1
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 12
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Legal Education Review (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Law › Legal Education and Practice Innovations
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- ChatGPT, generative AI, academic integrity, law school assessment, legal education, large language models, AI in teaching, ethics of AI use
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