Abstract
An intelligent teaching feedback system based on reinforcement learning algorithm is designed and implemented to meet the needs of personalized teaching. The system generates optimal feedback under different learning states by constructing user characteristic model and dynamic feedback strategy, which improves the learning effect and enhances the real-time response ability. Experiments show that the system outperforms traditional methods in terms of feedback accuracy, learning gain rate and response speed, with a feedback accuracy of 85.3% and an average response time of 120 ms. The results of the study provide technical support and empirical evidence for the personalized teaching mode, and propose a feasible direction for the optimization of the intelligent teaching system in the future.
| Original language | English |
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| Title of host publication | Proceedings of 2024 3rd International Conference on Artificial Intelligence and Education, ICAIE 2024 |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 563-566 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798400712692 |
| DOIs | |
| Publication status | Published - 29 Apr 2025 |
| Externally published | Yes |
| Event | 3rd International Conference on Artificial Intelligence and Education, ICAIE 2024 - Xiamen, China Duration: 22 Nov 2024 → 24 Nov 2024 |
Publication series
| Name | Proceedings of 2024 3rd International Conference on Artificial Intelligence and Education, ICAIE 2024 |
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Conference
| Conference | 3rd International Conference on Artificial Intelligence and Education, ICAIE 2024 |
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| Country/Territory | China |
| City | Xiamen |
| Period | 22/11/24 → 24/11/24 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Keywords
- Feedback Strategy Design
- Intelligent Teaching Feedback
- Personalized Teaching