arXiv:2605.29582v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise as educational tutors, yet effective tutoring requires more than solving problems: it must provide progressive Socratic guidance and balance multiple pedagogical objectives across multi-turn interactions. However, training such tutors remains challenging due to limited-fidelity and weakly controllable student simulation, under-specified pedagogical reward modeling, and unstable multi-objective optimization. To overcome these limitations, we propose PEARL, a pedagogically aligned reinforcement learni
The rapid advancement and widespread adoption of LLMs in educational contexts necessitate more sophisticated methods for training them to provide effective, multi-turn, and pedagogically sound guidance.
Improving LLM tutoring capabilities through approaches like PEARL will be crucial for scalable and personalized education, potentially democratizing access to high-quality learning experiences.
The focus for LLM-based tutors shifts from mere problem-solving to complex multi-objective pedagogical alignment and Socratic guidance, enabling more effective and adaptive learning systems.
- · AI education platforms
- · Students
- · LLM developers
- · EdTech sector
- · Traditional tutoring services using unaugmented humans
- · Basic 'answer-providing' AI tutors
More effective and personalized AI tutors become widely available, leading to improved learning outcomes.
The role of human educators evolves to focus on higher-order pedagogical design and complex individual interventions.
Accessibility to high-quality education expands globally, narrowing knowledge gaps and potentially accelerating innovation across various fields.
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