Ethical Dimensions of AI Use in Indian Classrooms: Student Data Privacy vs. Personalisation under NEP 2020

Abstract

Artificial intelligence is increasingly being introduced into Indian school education as a means of supporting personalised learning, differentiated instruction, and more responsive classroom practice. At the same time, these systems depend on the collection and processing of extensive student data, raising ethical concerns about consent, transparency, privacy, and equity. This study examines the ethical tension between AI-enabled personalisation and children’s digital privacy in Indian classrooms under the National Education Policy 2020. Using an interpretive qualitative research design, the study draws on document analysis, semi-structured interviews with 24 teachers, school administrators, and EdTech practitioners, and two focus group discussions with 16 senior secondary students from Bihar and Jharkhand. The analysis identifies three interrelated themes. First, consent practices in school-based AI use are often limited, opaque, and poorly understood by teachers, students, and parents. Secondly, the educational benefits of personalisation are unevenly distributed, especially where infrastructural gaps, limited digital familiarity, and algorithmic bias affect learners from disadvantaged backgrounds. Thirdly, there is a notable gap between India’s emerging policy and legal framework for data protection and the practical realities of AI deployment in schools. The findings suggest that privacy and personalisation should not be treated as opposing goals. Instead, ethical AI use in education requires accessible consent mechanisms, transparent data practices, teacher preparation in data ethics, bias-aware system design, and stronger institutional guidance for schools. The study concludes that AI can support educational equity only when its design and implementation are aligned with children’s rights, pedagogical needs, and accountable governance within everyday classroom practice.