An Autonomous Multi-Agent Intelligent Tutoring System for Adaptive and Personalized Learning

Mrugesh Patel¹, Hemangini Patel², Zinal Solanki³

¹Assitant Professor, Department of Information Technology, C.K. Pithawala College of Engg. & Tech., Surat, India.
²Assitant Professor, Department of Computer Engineering, Vidhyadeep Institute of Engineering and Technology, Kim, India.
³Assitant Professor, Department of Information Technology, Shree Swami Atmanand Saraswati Institute of Technology, India.

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Abstract

The rising need for custom education shows flaws in typical classroom methods. Rather than uniform teaching, this research introduces an intelligent tutoring system that uses separate AI parts to change help depending on what each student requires. As it monitors kids’ activities, school results, or how they interact socially, the setup tweaks lessons and replies instantly. The approach links incentive-driven rules with shifting personal records since speech processing allows students to choose subjects without relying on instructors. The results show this method beats old-school rule-based teaching when it comes to holding students’ attention, helping them remember better, yet making learning faster. The data hints that agent-run tutoring could actually work well, scale up easily, plus tailor lessons to fit today’s online classrooms.

Keywords: Autonomous AI agents, intelligent tutoring systems, personalized learning, reinforcement learning, learning analytics.

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