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Improving Robot-Assisted Virtual Teaching Using Transformers, GANs, and Computer Vision
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AI
DOI：10.4018/JOEUC.336481.png)
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Abstract
En 中文
This study aims to enhance the efficacy of personalized learning paths by amalgamating transformer models, generative adversarial networks (GANs), and reinforcement learning techniques. To refine personalized learning trajectories, the authors integrated the transformer model for enhanced information assimilation and learning path planning. Through generative adversarial networks, the authors simulated the fusion and interaction of multi-modal information, refining the training of virtual teaching assistants. Lastly, reinforcement learning was employed to optimize the interaction strategies of these assistants, aligning them better with student needs. In the experimental phase, the authors benchmarked their approach against six state-of-the-art models to assess its effectiveness. The experimental outcomes highlight significant enhancements achieved by the authors' virtual teaching assistant compared to traditional methods. Precision improved to 95% and recall to 96%, and an F1 score exceeding 95% was attained.
Keywords:
computer vision assistance
multimodal perception
personalized learning path planning
robot decision making
transformer model
virtual robot teaching assistant
Journal
IF:
3.4
Papers:
561
Citations:
1.2K