What is the challenge that you are addressing in relation to AI?
University students often struggle to engage critically with dense academic journal articles because of the complexity of theoretical concepts, unfamiliar terminology, and cognitive overload.
While generative AI tools are increasingly used by students, there is limited evidence on how AI can support deep learning rather than simply reduce reading effort.
The challenge was to explore whether NotebookLM could scaffold students’ reading and conceptual understanding without replacing engagement with the original text. Specifically, this project explored whether NotebookLM could help students better understand complex research articles in International Business (IB) while encouraging active engagement with academic content and critical evaluation of AI-generated outputs.
How specifically have you addressed this challenge?
Students were asked to use NotebookLM to engage with selected IB journal articles and to explore its features, including AI-generated summaries, podcasts, mind maps, flashcards, and the Q&A function. They were encouraged to compare these outputs with the original articles and reflect on how the tool influenced their understanding, reading strategies, and critical thinking.
Data were collected through semi-structured interviews with students. This qualitative approach was adopted because NotebookLM grounds its responses in uploaded source material rather than open web content, reducing the likelihood of hallucinations while providing multiple representations of complex academic knowledge. The design was informed by research on active learning, cognitive load, and AI-assisted learning, which suggests that providing alternative ways of engaging with challenging material can improve comprehension while maintaining opportunities for critical evaluation. It also enabled students to experience and reflect on the opportunities and risks of AI-assisted reading rather than assuming that the technology was either inherently beneficial or harmful.,
Were your actions successful or not?
The findings indicate interesting results. Different NotebookLM features support different learning styles or preferences. It is especially good at providing audio-visual and interactive formats.
One theme that emerged from the interview data is that students personalise NotebookLM according to their preferred learning styles. Some students particularly valued the podcasts, mind maps, and flashcards for simplifying complex concepts, while others preferred the summaries and interactive Q&A function illustrating that the tool helped tailor content to divide preferences.
Another theme that emerged from the interview data was that AI reshapes – not replaces – critical thinking. Some students believed AI reduced the need for independent analysis, whereas others reported that comparing AI-generated summaries with the original articles encouraged them to evaluate evidence more critically and make informed decisions about what to include in their academic work.
Since AI shifts critical thinking from extracting information to evaluating information, this tension is a novel finding, and it suggests that AI can serve as a critical thinking enabler when scaffolded appropriately as part of the learning design.
Why did you address this challenge in this way?
This approach was chosen because students often need an accessible entry point into conceptually dense articles, while still needing to engage with the original argument and evidence. NotebookLM’s summaries, Audio Overviews, mind maps, flashcards and Q&A offered multiple routes into the same material and allowed students to select formats that suited their immediate purposes, such as initial orientation, clarification or revision.
The activity also made critical thinking visible: students had to judge the accuracy, relevance and completeness of AI-generated outputs, compare them with the source and decide which evidence to use. This design therefore treated AI as a scaffold for reading rather than a substitute for it.
The evidence base comprises qualitative student reflections and feedback describing how NotebookLM affected their understanding, confidence, reading practices and critical engagement. These accounts reveal both recurring benefits and contrasting experiences, including improved accessibility alongside concerns about over-reliance and reduced independent analysis.
What have you learned from the experience?
This project demonstrated that there is no single best way to integrate AI into learning. Students used NotebookLM differently depending on their learning preferences, with some favouring podcasts and visual tools, while others preferred summaries and Q&A. AI should therefore be presented as a flexible reading companion rather than a replacement for academic reading.
Importantly, critical thinking is not determined by the technology itself but by how learning activities are designed. Educators should encourage students to interrogate, verify and critique AI-generated outputs alongside the original sources to promote deeper learning and responsible use of AI.
Do you have examples of how you have used AI in your education, research or leadership roles within higher education? Please do submit your short case-studies to us
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