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AI providers as criminal essay mills? Large language models meet contract cheating law
Open accessPublished version of a preprint
Abstract
Academic integrity has been a constant issue for higher education, already heightened by the easy availability of essay mill and contract cheating services over the Internet. Jurisdictions across the world have passed a range of laws making it an offence to offer or advertise such services. Because of the nature of these services, which may make students agree to not submit work they create or support, some of these offences have been drafted extremely broadly, without intent or knowledge requirements. The consequence of this is that there sit on statute books a range of very wide offences covering the support of, partial or complete authoring of assignments or work.At the same time, AI systems have become part of public consciousness, particularly since the launch of chatGPT from OpenAI. These large language models have quickly become part of workflows in many areas, and are widely used by students. These have concerned higher education institutions as they highly resemble essay mills in their functioning and result.This paper attempts to unravel the intersection between essay mills, general purpose AI services, and emerging academic cheating law. We:. Analyse, in context, academic cheating legislation from jurisdictions including England and Wales, Ireland, Australia, New Zealand, US States, and Austria in light of how it applies to both essay mills, AI-enhanced essay mills, and general purpose AI providers. (Chapter 2). Examine and document currently available services by new AI-enhanced essay mills, characterising them and examining the way they present themselves both on their own websites and apps, and in advertising on major social media platforms including Instagram and TikTok. These include systems which both write entire essays as well as those designed to reference AI-created work, provide outlines, and to deliberately ‘humanise’ text as to avoid nascent AI detectors. (Chapter 3). Outline the tensions between academic cheating legal regimes and both AI-enhanced essay mills and general purpose AI systems, which can allow students to cheat in much the same way. (Chapter 4). Provide recommendations to legislators and regulators about how to design regimes which both effectively limit AI powered contract cheating without, as some current jurisdictions without accidentally bringing bone fide general purpose AI systems into scope unnecessarily. (Chapter 5)We make some important findings.Firstly, there is already a significant market of AI-enhanced essay mills, many of which are developing features directly designed to frustrate education providers’ current attempts to detect and mitigate the academic integrity implications of AI generated work.Secondly, some jurisdictions have scoped their laws so widely, that it is hard to see how ‘general purpose’ large language models such as Open AI’s GPT-4 or Google’s Bard would not fall into their provisions, and thus be committing a criminal offence through their provision. This is particularly the case in England and Wales and in Australia.Thirdly, the boundaries between assistance and cheating are being directly blurred by essay mills utilizing AI tools. Most enforcement, given the nature of the academic cheating regimes, we suspect will result from private enforcement, rather than prosecutions. These regimes interact in important and until now unexplored ways with other legal regimes, such as the EU’s Digital Services Act, the UK’s proposed Online Safety Bill, and contractual governance mechanisms such as the terms of service of AI API providers, and the licensing terms of open source models.
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Legal uncertainty raises questions of platform liability, especially as students increasingly cognitively offload academic work to AI.
“The authors argue this legal uncertainty raises questions of platform liability, especially as students increasingly “cognitively offload” academic work to AI.”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [12]
Chapter 4 (p. 29) predicts that students will increasingly use AI for academic cheating, and the introduction (p. 7) describes students using AI to create work without intellectual engagement. The concluding remarks discuss the difficulty of holding AI providers liable. The claim presents increasing cognitive offloading as an established trend, whereas the paper frames that increase as a prediction. (LLM, conf=0.82)
Experimental — accuracy not yet measuredExisting contract cheating laws are insufficient to address the challenges posed by general-purpose AI tools such as ChatGPT.
“However, existing contract cheating laws are insufficient to address the challenges posed by general-purpose AI tools, such as ChatGPT [12].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [12]
General-purpose AI tools like ChatGPT may inadvertently fall within the scope of contract cheating laws, such as England's Skills and Post-16 Education Act.
“From a legal standpoint, general-purpose AI tools like ChatGPT may inadvertently fall within the scope of contract cheating laws, such as England's Skills and Post-16 Education Act [12].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [12]
Legal ambiguity exists around the use of AI tools in academic settings, particularly where generated content could be considered part of an assignment that a student is required to complete personally.
“Although AI providers do not market themselves as essay mills, the paper highlights the legal ambiguity around their use in academic settings, particularly where the generated content could be considered part of an assignment that a student is required to complete personally [12].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [12]
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Citations
Cites
Regulating ChatGPT and other Large Generative AI Models
Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466 · cited 1×
“155 They may not benefit from immunities, but also will not be subject to new proactive obligations, as other intermediaries have been.”
Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI
Widder, D. G., West, S., & Whittaker, M. (2023) · doi:10.2139/ssrn.4543807 · Cited by 87
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Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education
Deep, P. D., Edgington, W. D., Ghosh, N., & Rahaman, M. S. (2025) · Information · doi:10.3390/info16100905 · Cited by 26
Metadata
- Authors
- Gaumann, N., & Veale, M.
- Affiliations
- University College London (GB)
- Corresponding author
- Veale, Michael
- Venue
- Information & Communications Technology Law
- Publisher
- Informa UK Limited
- Published
- 2024-05-20
- Volume
- 33
- Issue
- 3
- Pages
- 276-309
- ISSN
- 1360-0834 (ISSN-L)
- 1469-8404
- OpenAlex
- W4398131962
- Cited by
- 17
- References
- 2
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 7
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information & Communications Technology Law (publishedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- SocArXiv (OSF Preprints) (submittedVersion, cc-by-nc-nd)
- Funders
- UKRI
- Trustworthy Autonomous Systems Hub
- UCL ChangeMakers fund
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Social Sciences › Law › Legal Issues in Education
- Keywords
- contract cheating, essay mills, academic integrity, large language models, ChatGPT, symbolic criminal law
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