AI For Bridging Socio-Economic Inequities in Indian Education Space

Over the past few years, Artificial Intelligence (AI), data analytics over edge and computer vision have ushered in a plethora of use cases that promise to transform the education landscape, particularly in the developing economies. The significant headways that AI has made in improving the quality of educational pedagogies and in enhancing the accessibility and inclusivity of the education models are now compelling the multiple stakeholders in the education space to reimagine the way learning is done and facilitated. Vast socio-economic inequities exist in the Indian education landscape, with poor infrastructural facilities, disparities in the technological competence of teachers and the gender & caste related divides plaguing the education space since decades. With a one-size-fits-all approach implemented for learning across the nation and with superficial insights into individual student’s cognitive capabilities, the education methodology in India faces serious limitations as students don’t receive personalized attention which leads to many students staying bereft of the value of education programs in the country. Artificial Intelligence enabled solutions can help identify the key areas of improvements in the education space, while analyzing the needs of each student in a personalized manner, to help every student derive the benefits of education, which can help bridge the socioeconomic inequities in the country. The report builds its arguments, analysis and future directions based on the data collected through secondary research. Industry benchmark reports by the leading consultancy firms and market research organizations will be leveraged, while also utilizing the data from open datasets on Unified District Information System for Education (UDISE), World Bank, NITI Aayog India and United Nations International Children’s Emergency Fund (UNICEF). Moreover, the authors have reached out to their respective common and divergent networks of educators, technologists and policymakers to understand the systematic approach that AI can take to transform the education landscape. The data-driven value case derivations quantify the benefits and opportunities for AI in education, which form the foundation of actionable and viable recommendations.