Abstract
Artificial intelligence (AI) has brought tremendous prospects and breakthroughs to a number of areas, including education. With an emphasis on the use of chatbots, analytics, generative AI, and personalized learning experiences, this research study offers a thorough analysis of the effects of AI on education. In order to shed light on the ethical implications, cultural considerations, language competence issues, and privacy concerns related to the use of AI in education, it explores the related limitations, obstacles, and concerns. Artificial Intelligence (AI) has the potential to completely transform higher education by promoting efficiency, creativity, customization, and engagement. Higher education could undergo a significant transition with the use of Generative Artificial Intelligence (GAI) tools as ChatGPT, Google BARD, and Bing Chat. But this integration also presents problems for avoiding plagiarism and upholding academic integrity. Within this In this work, we explore and evaluate useful strategies for effectively utilizing GAI's potential while also guaranteeing assignment integrity. We present the PAIGE (Promoting Assignment Integrity using Generative AI in Education) conceptual framework as a viable means of addressing these issues. This concept places a focus on the moral. The inclusion of GAI, encourages student engagement, and fosters chances for collaborative learning. Institutions of higher learning can efficiently use the promise of GAI while maintaining assignment integrity by utilizing the PAIGE framework. A responsible and prosperous future in education powered by generative AI is made possible by this strategy. The research report also explores the roles that parents, legislators, and educators play in minimizing the risks and optimizing the advantages of implementing AI in the classroom. Challenges with linguistic ability, privacy, and other factors related to using AI in education. Key Words: Generative AI, analytics, learning experiences, cognitive achievement, AI in the classroom, customized feedback
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