Artificial Intelligence Ecosystem for Automating Self-Directed Teaching
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912115515719680 |
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| author | Gotavade, Tejas Satish |
| author_facet | Gotavade, Tejas Satish |
| contents | 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 by presenting a comprehensive framework that combines automated content generation, visual learning aids, and intelligent tutoring to create an efficient, scalable solution for modern educational needs. Preliminary findings suggest that this approach not only accommodates diverse learning styles but also strengthens student engagement and knowledge retention through its emphasis on self-paced, independent learning methodologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07300 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence Ecosystem for Automating Self-Directed Teaching Gotavade, Tejas Satish Artificial Intelligence Computers and Society Machine Learning 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 by presenting a comprehensive framework that combines automated content generation, visual learning aids, and intelligent tutoring to create an efficient, scalable solution for modern educational needs. Preliminary findings suggest that this approach not only accommodates diverse learning styles but also strengthens student engagement and knowledge retention through its emphasis on self-paced, independent learning methodologies. |
| title | Artificial Intelligence Ecosystem for Automating Self-Directed Teaching |
| topic | Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2411.07300 |