Agentic Retrieval and Reinforcement Learned Equation Chains: A Controlled Generation Framework for Complex and Novel Physics Word Problems
arXiv:2606.15591v1 Announce Type: cross Abstract: Generating high-quality Physics Word Problems (PWPs) that are novel, complex, and solvable remains a challenging and underexplored problem in educational content generation. Existing approaches, many adapted from Math Word Problem (MWP) generation, often produce ambiguous, unsolvable, or structurally simple questions with limited linguistic diversity. We introduce ARVRE (Agentic Retrieval Value Reinforced Equation-chain), a two-stage framework for generating diverse and mathematically valid PWPs. In the first stage, a form of offline temporal-d
The rapid advancements in AI, particularly in large language models and reinforcement learning, are enabling more sophisticated and autonomous content generation systems.
This development represents a significant step towards AI systems that can generate complex, novel, and solvable educational content, potentially transforming teaching methods and assessment generation.
The ability to automatically generate high-quality, diverse physics word problems using agentic retrieval and reinforcement learning could alleviate a major bottleneck in STEM education content creation.
- · Education technology companies
- · AI content generation developers
- · STEM educators
- · Students
- · Traditional content creators (manual problem setters)
Improved efficiency and scale in generating educational materials for complex subjects like physics.
Personalized and adaptive learning systems could become far more sophisticated by dynamically generating problems tailored to individual student needs.
The widespread availability of high-quality, AI-generated educational content could democratize access to advanced learning, potentially reducing educational disparities globally.
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