Learner's Data at Risk

Artificial intelligence is transforming education into a data-intensive socio-technical environment in which learners are continuously monitored, classified, and governed through algorithmic systems. While AI-enabled tools promise efficiency and personalisation, they rely on extensive behavioural, biometric, and inferential data extraction that fundamentally reshapes learning. This chapter analyses how learning analytics, remote proctoring, biometric attendance, emotion-recognition technologies, and predictive risk platforms generate algorithmic epistemic authority within classrooms. Drawing on human-rights frameworks including the GDPR, UNESCO's Recommendation, OECD's Trustworthy AI principles, and regulatory approaches in India, the United States, the United Kingdom, China, and Brazil, supported by case studies. These cases demonstrate how governance failures, security breaches, and opaque vendor ecosystems erode learner data rights. The chapter argues that consent-based compliance is structurally inadequate in education and proposes a fiduciary, rights-respecting governance model