Evolutionary Reinforcement Learning based AI tutor for Socratic Interdisciplinary Instruction

This paper proposes ERL4SIIP, an Evolutionary Reinforcement Learning framework for AI tutors that implements Socratic teaching methods in interdisciplinary STEM education, specifically designed to foster higher-order cognitive abilities like knowledge integration and critical thinking while avoiding 'spoon-feeding' behaviors. The system uses dynamic student modeling, hierarchical rewards, and combines evolutionary algorithms with PPO to generate diverse teaching strategies that promote active learning rather than passive answer delivery.

Cultivating higher-order cognitive abilities -- such as knowledge integration, critical thinking, and creativity -- in modern STEM education necessitates a pedagogical shift from passive knowledge transmission to active Socratic construction. Although Large Language Models (LLMs) hold promise for STEM Interdisciplinary education, current methodologies employing Prompt Engineering (PE), Supervised Fine-tuning (SFT), or standard Reinforcement Learning (RL) often fall short of supporting this parad

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