Ethics of Artificial Intelligence for Analysing Student Learning Behaviour: A Multi-Stakeholder Perspective
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2025
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Melbourne Authors
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Computing and Information Systems
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PhD thesis
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Open Access
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© 2025 Bingyi Han
Abstract
The integration of Artificial Intelligence into Education (AIEd) promises enhanced personalisation, educational efficiency, and learning outcomes, yet raises complex ethical challenges that warrant scrutiny. One important challenge is the growing use of AI sensing technologies that monitor and analyse student learning behaviour and emotional states to inform teaching interventions. However, the rapid rise of these AI-enabled sensing and intervention technologies has outpaced a comprehensive understanding of their broader implications. For instance, the adoption of these technologies potentially impacts learners’ autonomy and educational interactions. Existing ethical discussions and frameworks predominantly focus on computational concerns such as data privacy, algorithmic bias and fairness. However, they often neglect the lived experiences of people affected by the system, particularly students, the primary recipients of its impacts. Students’ perspectives and concerns remain underexplored. Moreover, AIEd adoption involves multiple stakeholder groups whose competing values and power dynamics shape design and implementation processes. How these actors negotiate ethical considerations, and how such decisions may contribute to or overlook ethical risks, remains unknown. Without insights into both the consequences of AIEd adoption and the dynamics behind decision-making, current approaches risk failing to safeguard students from unintended harm.
This thesis addresses these issues through a bottom-up exploration of how different stakeholder groups perceive and respond to AIEd and its impacts. It investigates three core questions: What are the ethical challenges of AIEd from students’ perspectives? How do stakeholder dynamics shape ethical risks? And what values do students prioritise in AIEd adoption? This thesis comprises three empirical studies involving 71 student-generated narratives, 30 semi-structured interviews, and 131 survey responses collected through an online experiment.
Study 1 employs a speculative story-completion method to explore students’ perceptions of AIEd. It reveals concerns about psychological discomfort, behavioural change, diminished learning autonomy, altered pedagogical landscape, and changing social dynamics in AI-mediated classrooms. It underscores the complex, interwoven impacts of AIEd beyond technical or functional considerations.
Study 2 uses semi-structured interviews with developers, parents, and school decision-makers to examine their ethical considerations in AIEd design and adoption. It uncovers critical ethical gaps rooted in power imbalances and divergent priorities amongst stakeholders. It reveals the systematic exclusion of students’ perspectives and a significant accountability gap that fails to protect them from potential harm.
Study 3 employs a mixed-methods online experiment to investigate students’ affective responses, beliefs about learning, and ethical priorities in relation to specific AIEd features. It validates the findings of Study 1 about emotional discomfort regarding monitoring features and shows a strong student preference for autonomy and privacy over presumed learning benefits. This misalignment with institutional priorities identified in Study 2 is not just a difference in preference but a critical ethical fault line that, if unaddressed, may erode students’ trust, engagement, and well-being.
Together, these studies contribute novel empirical evidence that advances ethical, human-centred, and context-sensitive AIEd design and governance. The thesis extends current AIEd ethics discourse by foregrounding students’ lived experiences and advocating for emotional well-being and user agency as central to responsible AIEd development. It calls for governance frameworks that are inclusive, reflexive, and responsive to those most affected yet least empowered in technology adoption.
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Artificial Intelligence in Education (AIEd), Educational Technology, AI Ethics, Human–AI Interaction, Student-Centred AI Design, Student Autonomy and Privacy, Human Factors in AI