STUDENT DATA PRIVACY IN AI-DRIVEN PERSONALIZED LEARNING PLATFORMS: AN ETHICAL FRAMEWORK FOR HYBRID SCHOOLS
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Artificial Intelligence, Ethical Framework, Hybrid Education, Personalized Learning, Student Data Privacy
Abstract
The integration of artificial intelligence (AI) in personalized learning platforms has transformed hybrid education by enabling adaptive instruction, data-driven assessment, and individualized student support. However, this advancement has raised critical ethical concerns regarding student data privacy, transparency, and accountability. The unregulated collection, processing, and storage of learning data risk compromising students’ autonomy and confidentiality, particularly in hybrid schools where both digital and physical systems intersect. This study aims to develop an ethical framework that ensures responsible AI implementation in personalized learning environments while safeguarding student data integrity in Indonesian hybrid schools. A qualitative-descriptive research design was employed, involving document analysis, expert interviews, and focus group discussions with educators, AI developers, and policymakers. The research adopted a grounded theory approach to construct the framework, emphasizing ethical dimensions such as informed consent, algorithmic transparency, data minimization, and institutional accountability. Findings reveal that existing school policies often lack clarity in regulating third-party AI systems and data-sharing practices. The proposed ethical framework integrates three key components: governance principles, operational safeguards, and digital literacy strategies for teachers and students. The results suggest that adopting this framework can promote ethical awareness and responsible data stewardship, strengthening trust between institutions and learners. The study concludes that balancing innovation and ethical responsibility is essential to achieving equitable and secure AI-driven hybrid education.
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Copyright (c) 2025 Abel Mwansa, Alice Mutale, Julius Banda
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