Abstract
As artificial intelligence (AI) tools become increasingly embedded in assessment and feedback systems, understanding how students perceive these technologies is vital for maintaining trust and fairness in higher education. This study investigates how students experience AI-assisted grading through an extended Technology Acceptance Model (TAM) that incorporates fairness, transparency, and trust as pedagogically relevant constructs. Survey data from undergraduate marketing students (N = 142) were analyzed using cluster analysis, revealing three distinct perception profiles: Enthusiasts, Pragmatists, and Skeptics. These clusters differ significantly in their willingness to rely on AI feedback, perceived fairness of algorithmic grading, and expectations of instructor involvement. The findings highlight the need for educators to balance technological efficiency with human oversight and communication about fairness and transparency. Practical implications are offered to guide instructors in designing AI-mediated assessment practices that foster inclusion, trust, and equitable learning outcomes.
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Creative Commons License
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DOI
10.20429/jamt.2026.130005
Publication Date
4-2026
First Page
92
Last Page
111
Recommended Citation
Bello, R. (2026), Clusters of student perceptions of AI-assisted grading: Implications for fair and transparent assessment practice in higher education. Journal of Applied Marketing Theory, 13(0), 91-111. DOI: 10.20429/jamt.2026.130005 Available at: https://digitalcommons.georgiasouthern.edu/jamt/vol13/iss0/5