Using NotebookLM as a reading tutor for academic literature

The challenge was helping students engage with complex academic reading without becoming overwhelmed. When theory appears early in a text, students can disengage or skip it entirely before reaching content they might find more accessible. I wanted a way to help students enter difficult texts with enough orientation to stay with them, not to bypass the reading, but to approach it more confidently.

Before introducing NotebookLM, I asked my students to describe in their own words the "pain words" that came to mind when they thought about academic reading, and to rate themselves as academic readers. These student responses shaped how I approached the using NotebookLM.

I designed a structured reading sequence around NotebookLM. Students would first read the abstract, introduction, and conclusion of an article and write a single sentence summarising what they thought it was about. They would then upload the article. The example shown here involved a research article on Singapore, and they began using the tool to build their understanding.

Students could ask NotebookLM to translate a quick summary into another language, to explain a theoretical framework in more accessible terms, or to answer contextual questions like: "Why does this article matter in the context of a study on violence?" Once students were assigned multiple readings for a week, they could add those sources to the same notebook and ask: "What do these articles have in common?"

I was explicit that use of the tool was optional. Students were shown how it worked and encouraged to use it if they found it helpful, but it was not compulsory.

Watch the video below to see the NotebookLM outputs and what they looked like

This use case has strong implications for equity and access. It illustrates how NotebookLM can scaffold difficult academic reading for students from diverse disciplinary and language backgrounds, not by removing the challenge, but by lowering the barrier to entry. It also offers a practical model for teaching prompting as part of disciplinary reading skills, integrated into the subject matter rather than added as a generic AI activity.

• I engaged directly with the concern that students might use the tool to avoid reading altogether, becoming dependent on AI summaries rather than developing their own academic reading skills. My conclusion was that the purpose of the task mattered most: if NotebookLM helped students access the key ideas and engage with the concepts, it was serving a legitimate educational function. The tool was not the endpoint; it was a scaffold.
• I also emphasised that students still had to engage with the material itself. The unit required a portfolio, with time set aside in each workshop for students to reflect on how readings prompted them to think differently. There was also a viva connected to a review essay, meaning students had to demonstrate substantive engagement with the texts.