AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities
Bingyi Han, Ying Ma, Simon Coghlan, Dana McKay, George Buchanan, and Wally Smith

TL;DR
This study explores Australian university students' perceptions of AI sensing in learning, revealing preferences for targeted help over monitoring, and emphasizing the importance of privacy, autonomy, and ethical design in AI educational tools.
Contribution
It provides empirical insights into student attitudes towards AI sensing and intervention, highlighting ethical priorities and preferences for non-intrusive, customizable educational AI systems.
Findings
Students prefer system-generated hints over teacher-initiated help.
Students respond negatively to AI monitoring regardless of sensing method.
Ethical priorities include autonomy, privacy, transparency, and fairness.
Abstract
AI technologies that sense student attention and emotions to enable more personalised teaching interventions are increasingly promoted, but raise pressing questions about student learning, well-being, and ethics. In particular, students' perspectives about AI sensing-intervention in learning are often overlooked. We conducted an online mixed-method experiment with Australian university students (N=132), presenting video scenarios varying by whether sensing was used (in-use vs. not-in-use), sensing modality (gaze-based attention detection vs. facial-based emotion detection), and intervention (by digital device vs. teacher). Participants also completed pairwise ranking tasks to prioritise six core ethical concerns. Findings revealed that students valued targeted intervention but responded negatively to AI monitoring, regardless of sensing methods. Students preferred system-generated hints…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Emotion and Mood Recognition · Online Learning and Analytics
