Date
Publisher
arXiv
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.
What is the application?
Who age?
