iTutor: Promoting AI Guided Knowledge Interaction in Online Learning

Abstract

Although online education can balance educational resources and promote educational equity, it lacks interaction between teachers and learners. AI-based tutor systems may ease the unbalanced distribution of teachers, yet studies rarely examine AI-guided knowledge interaction in online learning as a potential factor in online platforms. To address this research gap, this study examined the experience and critical requirements of knowledge interactive tutor systems. Based on survey experiences and findings, we construct an Intelligent Tutor system (iTutor), a conversational agent tool that uses a real-time stream to enhance the learning experience. Relying on the AI technology, iTutor can build dynamic interactive support for online users' autonomous learning, including guide support, resource support, conversational interactive support, evaluation support, and feedback support. We conduct experiments to evaluate the acceptance, factors, and impact of AI-driven assistant tools in online learning. Results demonstrate that iTutor can affect learners' satisfaction with promoting online interactive feedback. These results highlight the critical factors affecting learners' satisfaction and the design frameworks for improving AI-guided knowledge teaching interaction in online learning.