• Sinchana
• Sudarshan K
• Jayashree K R

Integration of generic artificial intelligence (Gen AI) and large language models (LLMS) in education has created global interest due to their transformative ability in teaching, learning and research. This review synthesizes recent empirical studies and conclusions of pilot implementation in various educational contexts, especially in higher education and engineering subjects. Using the installed technology adopting model (TAMS), the review examines the major factors affecting the Gen AI adoption, including alleged utility, ease of use, social impact, facility and resistance factors. Highlight applications include AI-Assisted Research Partner, adaptive learning system and AI-supported evaluation environment. While important benefits such as students’ engagement, personal teaching, and automation of educational functions are clear, challenges remain around morality, educational integrity, equity and institutional readiness. Paper infrastructure underlines the importance of policy framework for development, educator training, clear ethical guidelines and responsible integration. This ensures action for stakeholders to navigate the developed landscape of AI-enhanced education by proposing actionable recommendations for stakeholders and for sustainable, justified adoption.

AI-assisted research, Adaptive learning systems, AI-driven assessment, Student engagement, Personalized learning, educational automation, Academic integrity, Ethical AI.

"GENERATIVE AI IN EDUCATION: A STUDENT-CENTRIC SURVEY ON OPPORTUNITIES AND CHALLENGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.131-136, July-2025, Available :http://www.jetir.org/papers/JETIRGX06025.pdf

"GENERATIVE AI IN EDUCATION: A STUDENT-CENTRIC SURVEY ON OPPORTUNITIES AND CHALLENGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp131-136, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06025.pdf