Developing an LLM-Based Feedback System Grounded in Evidence-Centered Design to Support Physics Problem Solving
arXiv:2512.10785v3 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 factually correct 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 and implementation of an LLM-based feedback system for physics problem solving grounded in evidence-centered design a
The rapid advancement and accessibility of large language models are pushing their application into more complex and domain-specific tasks, necessitating robust design principles.
This development indicates a maturation of AI feedback systems beyond simple conceptual tasks, moving towards more impactful applications in higher education and technical fields, potentially redefining tutoring and skill development.
The ability of LLMs to provide high-quality, 'expert-level' feedback is being validated and engineered, broadening their utility beyond general content generation to specialized pedagogical functions.
- · AI education platforms
- · Students in STEM
- · Educational technology providers
- · AI agent developers
- · Traditional tutors for complex subjects
- · Generic online course providers
- · Low-quality automated feedback systems
LLM-based feedback systems will become increasingly sophisticated, improving learning outcomes in challenging subjects.
This specialization will reduce the need for human intervention in foundational education, allowing educators to focus on advanced or idiosyncratic cases.
The democratization of expert-level feedback could narrow skill gaps in technical fields, impacting talent pools and labor markets globally.
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Read at arXiv cs.AI