NotebookLM as a Socratic physics tutor: Design and preliminary observations of a RAG-based tool

Abstract

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.