Exploring teachers’ AI literacy: cognitive, pedagogical, ethical, and contextual insights from Indian schools
Arnab Kundu, Tripti Bej, Rini Mandal · Teaching Education · 2025
This study investigates Indian school teachers’ AI literacy across cognitive, pedagogical, ethical, and contextual dimensions. Using an exploratory qualitative design, semi-structured interviews with 16 teachers from diverse school types and regions reveal that AI understanding is often superficial and tool-centric, with minimal hands-on exposure – especially in rural and government schools – indicating a critical cognitive-technical gap. Pedagogical use largely remains at substitution-level tasks (e.g. grading, content delivery), with little evidence of Substitution, Augmentation, Modification, and Redefinition (SAMR)-informed transformation. Ethical dilemmas – spanning data privacy, algorithmic bias, misinformation, and cultural misalignment – are heightened in settings lacking training and digital governance. Contextual disparities, including infrastructure, language accessibility, and regional policy support, further mediate engagement, with urban private schools showing relatively higher, though uneven, adoption. Findings highlight the need for equity-driven, culturally responsive, and ethically grounded professional development. Recommendations include embedding AI fundamentals and ethics into pre-service curricula, designing low-bandwidth multilingual training tools with simulations, piloting school-level data governance frameworks, and advancing regionally adaptive AI integration strategies – offering implications for other Global South contexts facing similar challenges.