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Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Open accessPreprint — published as 10.1080/14703297.2023.2190148 · Paper page
Abstract
The use of artificial intelligence in academia is a hot topic in the education field. The use of chatAPIs and GPT-3 in higher education has the potential to offer a range of benefits, including increased student engagement, collaboration, and accessibility. However, these tools also raise a number of challenges and concerns, particularly in relation to academic honesty and plagiarism. This paper examines the opportunities and challenges of using chatAPIs and GPT-3 in higher education, with a focus on the potential risks and rewards of these tools and the ways in which universities can address the challenges they pose. The paper discusses the main features and capabilities of chatAPIs and GPT-3 and provides examples of their use in higher education. It also considers the potential for these tools to be used for academic dishonesty and the difficulties of detecting and preventing such abuses. Finally, the paper suggests a range of strategies that universities can adopt to ensure that chatAPIs and GPT-3 are used ethically and responsibly, including developing policies and procedures, providing training and support, and using a variety of methods to detect and prevent cheating.
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In the citing paper
“Thus, use of ChatGPT can exacerbate fairness issues by putting non-users at disadvantage (Cotton et al., 2023), and ultimately reinforcing digital inequality (Fui-Hoon Nah et al., 2023; Mhlanga, 2023).”
From College students’ literacy, ChatGPT activities, educational outcomes, and trust from a digital divide perspective (2026) · cites this paper as Cotton et al., 2023
In the citing paper
“The need to rethink and revise the current assessment tools in health care education comes in light of ChatGPT’s ability to pass reputable exams (e.g., USMLE) and possibility of ChatGPT misuse, which would result in academic dishonesty [24,34,58,59,62,76,85,86,87].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [62]
In the citing paper
“As Cotton, Cotton, & Shipway (2024) note, AI-generated content challenges traditional definitions of authorship and originality, a concern echoed by Perkins (2023).”
From Generative AI-Enhanced Oral Exams in Economics Education (2026) · cites this paper as Cotton, Cotton, & Shipway (2024)
In the citing paper
“Incorporating real-time elements into assessments in the form of oral responses or monitored discussions can safeguard against unauthorized GenAI use and ensure a more accurate reflection of each student's understanding (Cotton, Cotton, & Shipway 2024).”
From Generative AI-Enhanced Oral Exams in Economics Education (2026) · cites this paper as Cotton, Cotton, & Shipway 2024
In the citing paper
“Because large language models (LLMs) excel at synthesizing domainspecific technical concepts and mimicking natural linguistic fluency, traditional written tasksuch as take-home assignments, reflective reports, or online language modules-are increasingly vulnerable to unearned authorship and cognitive shortcut (Cotton et al., 2023;Perkins, 2023).”
From Navigating Academic Integrity and Student Rationalisations of Generative AI in Vocational Online Learning Environments (2026) · cites this paper as Cotton et al., 2023
In the citing paper
“While the potential benefits of GenAI in education appear considerable, their role in assessments remains contentious (Cotton et al., 2023;Eke, 2023;Kasneci et al., 2023) and under-researched.”
From The rapid rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments (2024) · cites this paper as Cotton et al., 2023
In the citing paper
“However, one of the most significant concerns raised by educators is the potential threat GenAI poses to academic integrity (Cotton et al., 2023;Dergaa et al., 2023;Dwivedi et al., 2023).”
From The rapid rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments (2024) · cites this paper as Cotton et al., 2023
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Metadata
- Authors
- Cotton, D., Cotton, P., & Shipway, J. R.
- Affiliations
- Plymouth Marjon University (GB)
- University of Plymouth (GB)
- Corresponding author
- Cotton, Peter
- Venue
- Innovations in Education and Teaching International
- Publisher
- Center for Open Science
- Published
- 2023-01-10
- Volume
- 61
- Issue
- 2
- Pages
- 228-239
- ISSN
- 1470-3297 (ISSN-L)
- OpenAlex
- W4324046518
- Cited by
- 3,079
- References
- 24
- Citations by year
- 2026: 1105 · 2025: 801 · 2024: 789 · 2023: 381
- FWCI
- 769.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Innovations in Education and Teaching International (publishedVersion, cc-by-nc-nd)
- Plymouth Marjon University Repository (Plymouth Marjon University) (acceptedVersion)
- PEARL (University of Plymouth) (submittedVersion)
- EdArXiv (OSF Preprints) (submittedVersion)
- EdArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, academic integrity, academic dishonesty, cheating detection, plagiarism, artificial intelligence in higher education, AI ethics, university policy
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 ›
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