REINFORCEMENT LEARNING-BASED FRAMEWORK FOR EVALUATING AND ENHANCING TEACHER PERFORMANCE

ABSTRACT

Artificial Intelligence (AI) transforms education by enabling intelligent systems that support personalized learning, streamline administrative tasks, and enhance instructional delivery. Teacher performance, which is central to effective learning, is often evaluated through static and subjective methods that lack adaptability to classroom dynamics. The study proposes a reinforcement learning based framework for evaluating and enhancing teacher performance using policy-based approach. The framework models teaching behaviour and classroom interaction as a markov decision process, with states defined by engagement levels, class size, time of day, and difficulty level. Actions represent pedagogical strategies, while rewards capture changes in student engagement and instructional quality. In allowing an agent to interact with a simulated classroom and learn optimal teaching actions through trial and error, this approach enables dynamic adaptation. Although the theoretical foundation and preliminary implementation have been established, the research is ongoing. Future work will involve full-scale evaluation and validation using real or synthetic educational datasets.

Keywords: Reinforcement Learning, Teacher Performance Evaluation, Educational Systems, Adaptive Learning and Policy Optimization