AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A unified framework that models both student responses to questions and tutor-posed subtasks during dialogues on a shared item response theory (IRT) scale is proposed and results show that the framework outperforms strong baselines on both question difficulty prediction and response correctness prediction.
This work proposes a two-component framework in which a profile generator summarizes a student's history and a simulator predicts student turns conditioned on the resulting profile, which trains both components with reinforcement learning (RL), yielding profiles optimized for faithful student simulation.
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