A framework for integrating large language models in secondary physics education: practical design, opportunities, risks, and pedagogical principles

A framework has been proposed for integrating Large Language Models (LLMs) into secondary physics education, addressing the lack of structured guidance for their responsible and pedagogically sound deployment. This framework is rooted in established educational theories and identifies three core application modalities: teacher-facing instructional design tools, guarded student-facing inquiry tools, and assessment-augmentation tools. The analysis highlights both domain-specific opportunities, such as addressing misconceptions in mechanics and electromagnetism, and inherent risks including cognitive offloading, epistemic opacity, and algorithmic bias.

The responsible and effective integration of advanced AI technologies like LLMs into educational systems represents a critical strategic imperative for maintaining pedagogical relevance and fostering future-ready competencies. Understanding the opportunities and risks associated with these technologies is essential for developing robust, ethical, and effective educational strategies that leverage innovation while mitigating potential harms to learning outcomes and equity.

The rapid advancement of LLMs presents significant potential for transforming secondary physics education.

Forward consideration, not a verified fact.