Artificial Intelligence Ecosystem for Automating Self-Directed Teaching
Tejas Satish Gotavade

TL;DR
This paper presents an AI-driven educational ecosystem that personalizes self-directed learning through automated content, visualization, and virtual assistance, aiming to improve engagement and learning outcomes.
Contribution
It introduces a comprehensive AI-based framework integrating automated content creation, visualization, and tutoring to enhance self-paced, autonomous education.
Findings
Improves student engagement and retention
Accommodates diverse learning styles
Enhances autonomous learning practices
Abstract
This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generation, and three-dimensional modeling for complex concept visualization. By integrating real-time virtual assistance for doubt resolution, the platform addresses the immediate educational needs of learners while promoting autonomous learning practices. This study explores the psychological advantages of self-directed learning and demonstrates how AI automation can enhance educational outcomes through personalized content delivery and interactive support mechanisms. The research contributes to the growing field of educational technology…
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Taxonomy
TopicsAdvanced Data Processing Techniques
