Reinforcement learning (educational psychology) is an academic approach that applies the theoretical frameworks and computational methodologies of reinforcement learning, primarily derived from machine learning and artificial intelligence, to analyze, model, and enhance human learning processes within educational contexts. It conceptualizes the learner as an agent engaging in interactions with an educational environment, where actions (e.g., responses, study choices) lead to outcomes providing feedback that influences future behavior and cognitive performance, analogous to receiving rewards or penalties. This perspective is utilized to investigate human cognitive processes, such as reasoning and decision-making under uncertainty, understand how individuals adapt their strategies over time, and inform the design of adaptive instructional systems aimed at optimizing learning outcomes. Its significance lies in providing computational models for understanding complex learning behaviors, personalizing educational interventions, and developing intelligent tutoring systems.