Beyond access: a mixed-methods analysis into generative AI integration and educational equity

Résumé fourni par la source

Purpose This study aims to examine the impact of Generative Artificial Intelligence (GenAI) integration on educational equity across diverse socioeconomic contexts in India through a digital inequality framework in context to socioeconomic stratification. This study adopted an integrated theoretical framework for understanding the importance of GenAI in achieving better results in education using new technologies. Design/methodology/approach The mixed methods research involves 27 focus groups (133 educators) conducted in the National Capital Region in Delhi, the interviews with school heads and the performance testing of 587 students from different socio-economic schools conducted in 2023–2024. Findings Through analysis of technological capital, engagement ecology and policy implementation, GenAI Educational Equity Matrix is obtained, which consists of three interrelated aspects of equity. The results prove the difference in the readiness of AI based on socioeconomic status. Practical implications The research provides empirically based, policy-related suggestions regarding responsible usage of GenAI in education, which may be helpful for policymakers, school administrators and educators in different socio-economic settings and is relevant to NEP 2020 of India. Social implications This study illustrates how technology may increase or decrease educational inequalities, emphasizing the necessity of proper technology integration to overcome any barriers. Originality/value GenAI Educational Equity Matrix gives a new multi-dimensional perspective on right technology integration in education, especially in developing countries with digital divide.

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