Optimization of Intelligent Education Systems Based on Reinforcement Learning

Abstract

This paper explores how reinforcement learning (RL) can improve intelligent education systems. RL helps make learning personal, flexible, and efficient by choosing actions based on student needs and rewards like better scores or engagement. We study its use in custom learning paths, smart testing, and teacher support, showing how it beats old methods that don’t adapt. The paper also suggests future ideas—like better RL tools, teamwork learning, and mixing RL with big language models—while noting fairness challenges. Using pretend data with 1000 students, we test RL’s power to plan learning step by step. Results show RL can lift learning by 20–25% in areas like tutoring and class focus. This work gives a clear plan for using RL to make education smarter and fairer, pointing to a bright future for adaptive learning.