AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities

2026-02-11

AI Summary

This study addresses growing concerns among students regarding the learning efficacy, emotional impact, and ethical implications of AI-driven perception and intervention technologies in higher education, an area lacking systematic understanding from the student perspective. Employing a mixed-methods experimental design, it integrates student feedback on AI-based instructional interventions—grounded in attention or emotion recognition—across three dimensions: perceived learning impact, affective responses, and ethical considerations. Through an online video-based scenario experiment and a paired-comparison ranking task, the research compares sensing modalities (e.g., gaze tracking vs. facial expression recognition) and intervention agents (teacher-led vs. system-initiated). Findings reveal a clear student preference for automated system prompts over teacher intervention and widespread opposition to AI surveillance. Ethical priorities were ranked as autonomy, privacy, transparency, accuracy, fairness, and learning benefit, underscoring the critical need for designs that support learner autonomy and social sensitivity.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 over teacher-initiated assistance, citing learning agency and social embarrassment concerns. Students'ethical considerations prioritised autonomy and privacy, followed by transparency, accuracy, fairness, and learning beneficence. We advocate designing customisable, social-sensitive, non-intrusive systems that preserve student control, agency, and well-being.

Problem

• AI sensing
• student perceptions
• ethical concerns
• personalised intervention
• higher education

Innovation

• AI sensing
• student agency
• ethical priorities
• affective response
• personalised intervention