ChatGPT and Academic Integrity: A case study of Assessment Vulnerability in Biological & Environmental Sciences

Hinchcliffe, D ORCID: 0000-0001-5204-5161, Fergani, C and Zajitschek, SRK ORCID: 0000-0003-4676-9950 (2026) ChatGPT and Academic Integrity: A case study of Assessment Vulnerability in Biological & Environmental Sciences. Advances in Physiology education, 50 (4). pp. 1031-1109. ISSN 1043-4046

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Abstract

The rise of generative AI has accelerated content creation across sectors, including education and academia. While these tools can streamline workflows and support learning, their use by students to complete assignments poses risks to academic integrity. This case study quantitatively evaluated the vulnerability of existing coursework assessments and exams to cheating using generative AI. Specifically, we compared ChatGPT-4's performance to student grades on 131 assessments from 40 modules in the School of Biological & Environmental Sciences at a UK University. ChatGPT excelled in "exam-like" assessments, including coursework-associated tests, exams (particularly those involving multiple choice) and in some levels of study, essay-style assessments. Conversely, AI performance and hence vulnerability was low on assessments requiring in-class data collection, collaboration, or the creation of reports, posters, or presentations. Overall, ChatGPT-produced work was often superficial and vague, but better than expected, posing risks to assessing learning in all fields within the biological sciences, including physiology. While generative AI holds promise as a tool to help students understand, organise and structure, it raises concerns around misuse. Designing creative, individualised tasks that require genuine intellectual engagement is essential, with greater emphasis on ethical conduct and the importance of integrity. Synthesising information across multiple biological scales and applying clinical or experimental scenarios is recommended to develop higher-order physiological reasoning in students. Continuous, collaborative monitoring of generative AI performance on assessments should become part of routine assessment design and evaluation, given the continuous development and improvement of these tools. In cases where basic knowledge testing remains necessary, reversion to in-person examinations is recommended to replace online testing.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence; Assessment; Pedagogy; Quality Education; 39 Education; 3904 Specialist Studies In Education; Health Disparities and Racial or Ethnic Minority Health Research; Machine Learning and Artificial Intelligence; Health Disparities; Clinical Research; Minority Health; Generic health relevance; 4 Quality Education; 0606 Physiology; 1302 Curriculum and Pedagogy; 1303 Specialist Studies in Education; Education; 3901 Curriculum and pedagogy
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences L Education > L Education (General) L Education > LB Theory and practice of education Q Science > QH Natural history > QH301 Biology Q Science > QP Physiology T Technology > T Technology (General)
Divisions:	Biological and Environmental Sciences (from Sep 19)
Publisher:	American Physiological Society
Date of acceptance:	2 July 2026
Date of first compliant Open Access:	9 September 2026
Date Deposited:	09 Sep 2026 15:37
Last Modified:	09 Sep 2026 15:37
DOI or ID number:	10.1152/advan.00273.2025
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29367

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