AI in action: Changes to student perceptions when using generative artificial intelligence for the creation of a multimedia project-based assessment
Open accessAbstract
INTRODUCTION: New modes of assessments are needed to evaluate of the authenticity of student learning in an artificial intelligence (AI) world. In mid-2023, we piloted a new assessment type; a collaborative group multimedia assessment with AI allowance. The aim of the research study was to explore the experiences of students using AI in a multimedia assessment. We further aimed to determine whether these use cases changed student perceptions of the ways AI can be used in learning and assessment. METHODS: Students enrolled in a capstone Pharmacology interdisciplinary unit (n = 40) were included in an exploratory, qualitative case study methodology. Thematic analysis using an AI role-based conceptual framework was used to explore student perceptions of AI use prior to and during their projects from logbooks documenting the assessment process. RESULTS: AI was initially perceived by students as having a personal tutor-style role, which aligned with the taxonomy with AI acting as an Arbiter (49 %), Oracle (41 %) and Quant (10 %). In contrast to their earlier perceptions, AI was only used in a limited manner in the early stages of assessment in the idea generation in the role as an Oracle (86 %) or in data analytic purposes as a Quant (14 %), (n = 14 cases in 5 groups). No student group used AI to generate written text for the final assessment. DISCUSSION: Tension between perceived and actual use of AI is indicative of the uncertainty faced by students with the allowance of AI within assessments. Clear guidance for educators and students about how to assess the AI-supported learning process is needed to ensure the integrity of the assessment system.
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AI-assisted assessments may create an unfair academic playing field by benefiting students with better access to AI tools and technology while disadvantaging those from underprivileged backgrounds.
“Additionally, AI-assisted assessments may create an unfair academic playing field by benefiting students with better access to AI tools and technology while disadvantag -ing those from underprivileged backgrounds [ 62, 64, 97].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [62]
Faculty training programs have been developed to help educators recognise AI-generated content, redesign assessment methods, and integrate AI responsibly into teaching and evaluation processes.
“Faculty training programs have been developed to help educators recognise AI-generated content, redesign assessment methods, and integrate AI responsibly into teaching and evaluation processes [ 62, 67].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [62]
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- Authors
- Charles, K. A., Yousuf, A., Chua, H. C., Matthews, S., Harnett, J., & Hinton, T.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Charles, Kellie A.
- Venue
- European Journal of Pharmacology
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 998
- Pages
- 177508
- ISSN
- 0014-2999 (ISSN-L)
- OpenAlex
- W4408348316
- Cited by
- 17
- References
- 17
- Citations by year
- 2026: 9 · 2025: 8
- FWCI
- 17.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Pharmacology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- generative AI, student perceptions of AI, AI-assisted assessment, assessment integrity, pharmacology education
- MeSH terms
- Generative Artificial Intelligence, Artificial Intelligence, Educational Measurement, Female, Humans, Male, Perception, Students, Students, Pharmacy, Multimedia
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