Adaptive learning, characterized by the functionality of customizing learning tasks based on learners’ characteristics, has been a promising area in STEM education. This chapter demonstrates how reinforcement learning (RL) drives algorithmic innovation in adaptive learning systems. We used simulation-based studies to examine how learner state representation—whether discrete or continuous—shapes the effectiveness of learning task recommendation strategies. Leveraging the DINA and M3PL models, we implemented and evaluated RL-driven approaches (Q-learning and actor-critic algorithms) under varying conditions of measurement error. Our findings underscore not only the superiority of RL-based strategies over random baselines but also their resilience in noisy assessment environments. Beyond demonstrating technical performance, this chapter contributes a methodological blueprint for developing and validating adaptive learning algorithms in controlled simulation environments. The chapter concludes by identifying emerging trends and research imperatives, including the ethical use of learner data and hybrid human-AI instructional models. Together, these insights position algorithmic adaptivity as a cornerstone for future-ready STEM education.