Banjade, Shivraj, Patel, Hiran and Pokhrel, Sangita ORCID: https://orcid.org/0009-0008-2092-7029 (2024) Empowering Education by Developing and Evaluating Generative AI-Powered Tutoring System for Enhanced Student Learning. Journal of Artificial Intelligence and Capsule Networks, 6 (3). pp. 278-298.

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Abstract

Personalized learning has always been a dream for schools, educators, and students but until recently, educators didn’t have the time or resources to implement it on a large scale. With the advancements in AI, Generative AI can automate many of a teacher’s core tasks, such as creating lesson resources. providing lesson structures and key talking points, designing infographics, creating slideshows, and converting text into videos and images. This study details the development and evaluation of an AI-powered tutoring system designed to enhance student learning experiences. Motivated by the transformative potential of AI in education, the research aims to utilize large language models, including OpenAI, to create a personalized and adaptive learning environment. The research is a two-phase approach, involving a comprehensive literature review, problem definition, and AI integration in the Research Phase, followed by design, prototyping, and testing in the Design and Development Phase. The course creation workflow emphasizes the collaborative efforts of human tutors and AI algorithms using the GPT-3.5-Turbo model. The study identified the potential improvement in education where the course has been created by AI including the image generated by DALLE-3 and contributing to the evolving landscape of AI-assisted education using the text-to-voice, an automatic speech recognition system by Whisper, offering an innovative and transformative learning experience for students and tutors. The course content has question-answering chatbots where the students can ask any questions related to the topic while learning.

Item Type:	Article
Status:	Published
DOI:	10.36548/jaicn.2024.3.003
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/10583

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