Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback
Jasper Roe (1), Mike Perkins (2), Daniel Ruelle (3) ((1) James Cook, University Singapore, (2) British University Vietnam, (3) VinUniversity)

TL;DR
This study explores perceptions and experiences of students and staff with AI and GenAI in higher education assessment, revealing low familiarity, mixed attitudes, and highlighting the need for policy development.
Contribution
It provides empirical insights into AI and GenAI perceptions in assessment, addressing a gap by comparing student and staff experiences across multiple universities.
Findings
Low familiarity with GenAI among participants
Negative perception of GenAI feedback, improved with instructor input
Greater acceptance of AI detection tools by staff than students
Abstract
The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform. This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor…
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Taxonomy
TopicsOnline Learning and Analytics
