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Study on the Impact of Artificial Intelligence on Student Learning Outcomes Sasikala, P.; Ravichandran, R.
Journal of Digital Learning and Education Vol. 4 No. 2 (2024): AUGUST
Publisher : MO.RI Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52562/jdle.v4i2.1234

Abstract

This study explores the transformative potential of Artificial Intelligence (AI) in education by analyzing its impact on student learning outcomes. Through a comprehensive literature review, the research synthesizes current findings on the integration of AI in educational settings, examining both the benefits and challenges it presents. The study explores into AI's role in personalizing learning experiences, enhancing student engagement, and improving academic performance. Ethical considerations such as data privacy and algorithmic bias are also assessed. This research also identifies existing gaps in the literature and suggests avenues for future inquiry, contributing to a deeper understanding of how AI can be effectively and responsibly integrated into education to optimize student success.
Harnessing Generative AI in Higher Education: Opportunities, Challenges, and Ethical Imperatives Ravichandran, R.; Sasikala, P.
Journal of Digital Learning and Education Vol. 5 No. 1 (2025): January-April
Publisher : MO.RI Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52562/jdle.v5i1.1455

Abstract

Generative Artificial Intelligence (AI) is poised to transform higher education, particularly in complex and diverse contexts such as India. This article critically examines the potential of generative AI to address longstanding challenges, including the digital divide, the need for scalable personalized learning, and the evolving role of educators. It explores how AI tools can enhance student engagement, support teacher empowerment, and enable inclusive learning, especially in under-resourced settings. The paper also interrogates key ethical concerns, such as academic integrity, data privacy, algorithmic bias, and equity, while analysing current government initiatives aimed at responsible AI adoption. Drawing on global comparisons and field-based perspectives, the article presents a balanced analysis of both the opportunities and limitations of AI in Indian higher education. It concludes by offering practical recommendations for policy, pedagogy, and future research to foster a more equitable, AI-enabled academic ecosystem