Personalized learning through AI within India's higher education sector presents immense transformative possibilities under NEP 2020 with potential for customization, prediction, and improved performance. However, deep-seated disparities between urban and rural areas, compounded by infrastructural deficiencies, teacher shortages, and poor policy integration, may hinder equitable implementation. This empirical research examines AI-enabled personalization in India's 1,200+ higher education institutions (HEIs) during 2025–2026 based on findings from FICCI-EY and AISHE studies revealing a 57% urban and 22% rural adoption rate. Utilizing quantitative trend analysis and qualitative thematic review approaches, it examines challenges such as rural internet infrastructure (35% penetration), AI proficiency (40% skilled staff), and ethical concerns like bias in testing. The project's aims include assessing adoption rates, NEP alignment, and strategic planning. Results recommend staged actions, such as infrastructure trials, compulsory teacher training, and expansion of the National AI Mission through PPP initiatives. Effective governance, particularly concerning data privacy and federal coordination comparable to GST mechanisms, is critical. Addressing these issues can unlock opportunities for 10 million rural students by 2030, aligning with Viksit Bharat.
S et al. (Thu,) studied this question.