NotebookLM as a Socratic Physics Tutor: Design and Preliminary Observations of a RAG-Based Tool

This study explores NotebookLM, a Google Gemini-powered AI platform, that integrates Retrieval-Augmented Generation (RAG) as a Socratic tutor for physics education. In this implementation, NotebookLM was configured to support students in solving conceptually oriented physics problems through a guided, questioning-based dialogue. When deployed as a collaborative tutor, the system restricts student interaction to a chat-only interface, promoting controlled and guided engagement. By grounding its responses in teacher-provided source documents, the AI tutor helps mitigate one of the major shortcomings of standard Large Language Models’ hallucinations, thereby ensuring more traceable and reliable answers. This work details the methodological design of the tutor, including the iterative development of a pedagogical “Training Manual”, and presents preliminary qualitative observations from demonstrations with pre-service and in-service teachers. These observations highlight both the promising potential of the tool and key pedagogical challenges, such as managing user motivation. While limitations remain, this work offers a promising and replicable model for educators seeking to implement grounded AI tutors in their own teaching contexts.