Integrating Generative AI Into University Coursework Assessments – a Student Perspective

Generative AI is reshaping higher education and prompting a re‑evaluation of traditional assessment practices. This study examines undergraduate university students’ experiences and perceptions of undertaking an AI-integrated coursework assessment, whereby students were tasked with critically evaluating ChatGPT generated scientific output. An AI-integrated assessment case study is presented, followed by a mixed-methods analysis of questionnaire data, open-ended responses, and focus group discussions. Students reported that generative AI supports university assessments through idea generation, summarisation, and clarification of complex concepts, but expressed concerns about academic integrity, and the reliability and scientific accuracy of AI generated outputs. AI‑enabled assessments were viewed positively as they encouraged critical evaluation of AI‑generated content, supported independent research and promoted understanding of the limitations of AI. However, students also identified potential long-term risks to critical thinking associated with AI use and emphasised the importance of clearer institutional guidance and further support for digital literacy development. Although the study was limited to a single student cohort, the findings suggest that thoughtfully integrating AI into the assessment structure can preserve critical thinking, promote AI literacy competencies and maintain assessment authenticity.

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Publication Details

Journal
Intersection A Journal at the Intersection of Assessment and Learning
Published
2026-06-29
DOI
https://doi.org/10.61669/001c.164293
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating Generative AI Into University Coursework Assessments – a Student Perspective

David Abraham, Andrew E. Williams, Angela Tam
Intersection A Journal at the Intersection of Assessment and Learning
Artificial Intelligence in Healthcare and Education
article

Integrating Generative AI Into University Coursework Assessments – a Student Perspective

David Abraham, Andrew E. Williams, Angela Tam
article en

Abstract

Generative AI is reshaping higher education and prompting a re‑evaluation of traditional assessment practices. This study examines undergraduate university students’ experiences and perceptions of undertaking an AI-integrated coursework assessment, whereby students were tasked with critically evaluating ChatGPT generated scientific output. An AI-integrated assessment case study is presented, followed by a mixed-methods analysis of questionnaire data, open-ended responses, and focus group discussions. Students reported that generative AI supports university assessments through idea generation, summarisation, and clarification of complex concepts, but expressed concerns about academic integrity, and the reliability and scientific accuracy of AI generated outputs. AI‑enabled assessments were viewed positively as they encouraged critical evaluation of AI‑generated content, supported independent research and promoted understanding of the limitations of AI. However, students also identified potential long-term risks to critical thinking associated with AI use and emphasised the importance of clearer institutional guidance and further support for digital literacy development. Although the study was limited to a single student cohort, the findings suggest that thoughtfully integrating AI into the assessment structure can preserve critical thinking, promote AI literacy competencies and maintain assessment authenticity.

Intersection A Journal at the Intersection of Assessment and Learning
University College London (GB)
Quality Education
Openalex Percentile: Top 11%
Artificial Intelligence in Healthcare and Education
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Integrating Generative AI Into University Coursework Assessments – a Student Perspective — David Abraham, Andrew E. Williams, et al. · Intersection A Journal at the Intersection of Assessment and Learning (2026) | TGRS Research Map | TGRS