Developing and Evaluating a Large Language Model-Based Automated Feedback System Grounded in Evidence-Centered Design for Supporting Physics Problem Solving
📰 ArXiv cs.AI
Developing an LLM-based automated feedback system for physics problem solving using evidence-centered design
Action Steps
- Design an evidence-centered framework for physics problem solving
- Train and fine-tune a large language model on a dataset of physics problems and solutions
- Develop an automated feedback system that generates feedback based on the LLM's output
- Evaluate the effectiveness of the feedback system using metrics such as accuracy and student satisfaction
Who Needs to Know This
AI engineers and educators can benefit from this study to create more effective feedback systems for complex domains like physics, improving student learning outcomes
Key Insight
💡 Evidence-centered design can be used to develop effective LLM-based feedback systems for complex domains like physics
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🤖 LLM-based feedback system for physics problem solving! 📝
Key Takeaways
Developing an LLM-based automated feedback system for physics problem solving using evidence-centered design
Full Article
Title: Developing and Evaluating a Large Language Model-Based Automated Feedback System Grounded in Evidence-Centered Design for Supporting Physics Problem Solving
Abstract:
arXiv:2512.10785v2 Announce Type: replace-cross Abstract: Generative AI offers new opportunities for individualized and adaptive learning, e.g., through large language model (LLM)-based feedback systems. While LLMs can produce effective feedback for relatively straightforward conceptual tasks, delivering high-quality feedback for tasks that require advanced domain expertise, such as physics problem solving, remains a substantial challenge. This study presents the design of an LLM-based feedback
Abstract:
arXiv:2512.10785v2 Announce Type: replace-cross Abstract: Generative AI offers new opportunities for individualized and adaptive learning, e.g., through large language model (LLM)-based feedback systems. While LLMs can produce effective feedback for relatively straightforward conceptual tasks, delivering high-quality feedback for tasks that require advanced domain expertise, such as physics problem solving, remains a substantial challenge. This study presents the design of an LLM-based feedback
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