From Models to Impact: A Systematic Review of Generative Artificial Intelligence in Education

Abstract

Generative Artificial Intelligence (GenAI) is increasingly influencing educational systems by enabling personalized learning, automated content generation, adaptive assessment, and intelligent tutoring. Despite its rapid advancement and growing adoption, the effective and responsible integration of GenAI in education remains a challenge due to ethical concerns, limited empirical validation, and issues related to user trust. This paper presents a systematic review and analysis of existing literature to examine the pedagogical potential, ethical risks, and trust-related challenges associated with GenAI-enabled educational environments. It further analyzes global and Indian adoption trends to highlight domain-wise and regional variations in GenAI usage. By identifying key research gaps in current studies, the paper emphasizes the need for integrated, trust-aware, and ethically aligned approaches. The study contributes by synthesizing insights from prior research and proposing directions for the sustainable and responsible deployment of Generative AI in education.

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