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Research Paper | Computer Science and Engineering | Volume 15 Issue 5, May 2026 | Pages: 686 - 691 | India
Generative Artificial Intelligence in Education from a Technological Research Perspective
Abstract: Generative Artificial Intelligence (GenAI) is transforming modern educational environments by enabling advanced tools capable of generating text, multimedia learning materials, and intelligent responses to student queries. Recent advancements in large language models, neural networks, and deep learning architectures have made it possible to create intelligent tutoring systems, automated grading platforms, and adaptive learning environments that respond to individual student needs [1]. This research paper explores generative AI in education from a technological research perspective by examining system architectures, methodological approaches, application domains, and empirical findings from recent studies [2]. A structured literature review and conceptual analysis of research published between 2020 and 2026 were conducted to identify emerging trends in generative AI?driven educational technologies. The results indicate that generative AI significantly enhances personalized learning, improves teaching efficiency, and expands educational accessibility [3]. However, challenges such as data privacy risks, academic integrity concerns, algorithmic bias, and ethical implications must be carefully addressed. The paper concludes that while generative AI has the potential to redefine the future of education, responsible design, transparent algorithms, and human?AI collaboration is essential for sustainable implementation.
Keywords: Generative Artificial Intelligence, AI in Education, Educational Technology, Large Language Models, Intelligent Tutoring Systems, Personalized Learning
How to Cite?: Imran Alam, Dr. Zeba Irfan, "Generative Artificial Intelligence in Education from a Technological Research Perspective", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 686-691, https://www.ijsr.net/getabstract.php?paperid=SR26508150807, DOI: https://dx.dx.doi.org/10.21275/SR26508150807