Reinforcement Learning-Driven Adaptive Curriculum Generation for Personalized STEM Education: A Bayesian Optimization Approach
Year: 2025
Type: article
Abstract: **Abstract:** This paper proposes a novel adaptive curriculum generation system for STEM education leveraging reinforcement learning (RL) and Bayesian optimization. Addressing limitations of traditional fixed curricula, our system dynamically adjusts the order, difficulty, and context of learning materials based on individual student performance and predicted cognitive load. The core innovation lies in the integration of a Bayesian optimization process to fine-tune the RL agent's exploration-exploitation strategy, resulting in faster convergence and improved student learning outcomes. Results demonstrate a significant improvement in knowledge retention and engagement compared to standard curricula, highlighting the potential for personalized STEM education at scale. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*
Source: Figshare
Authors: KYUNGJUN, LIM, Researcher, Freederia AI
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7075985649
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