Notebook LM is a mobile application that Google has created to utilize its AI, Gemini 2.0, to summarize and refine notes and other information sources. The note files can be in different forms (i.e., PDFs, Google Docs), but more importantly, audio files and YouTube can be easily summarized. The app can even create explicit connections between all the information for the user. Different from other resources, the app includes citations so that students are able to refer back to the original source of the information.
Check it out here: Notebook LM
One of its most powerful abilities is the ability to generate audio guides so that students will be able to study on the go. In fact, one of the perks of using this feature is that conversations can be had with Gemini so that students can ask questions or use it as a quizzing feature where they are asked questions.
However, more features come with a cost. Alarmingly, it is only the updated version where there is ‘better data protection’ for the user.
As a result, a few questions come to mind:
1.) Will students merely memorize what has been summarized for them when they do not have to do the work of understanding the material themselves? Will students further lose the ability to create new and creative connections when it is done for them?
2.) What is the information that Google is collecting and being used for? Should the fact that an upgrade is required for better security be condoned legally?
This post is excellent for surfacing both the opportunities and challenges of Notebook LM. I appreciated how you highlighted not only its summarization power, but also its unique features like audio guides, conversational quizzing, and citations. Those concrete details make it easy to see real applications for mobile learning.
What stood out even more were your critical questions. The concern about students skipping the hard work of understanding is timely—if AI reduces friction too much, we risk losing the “productive struggle” that deepens learning. Your privacy question is also vital: why should stronger data protection be locked behind an upgrade?
At my workplace, we’re actually exploring NotebookLM to pilot podcasts for students on specific course topics. We’ve seen the potential, but copyright and data privacy concerns are holding us back for now. Interestingly, we also think AI may create more work, since it gives us new ways to remix and expand content, while also helping us get it to more students faster.
This is one of the few AI study tools that natively respects citations and multimodal inputs (PDFs, Docs, YouTube, even audio)—and the audio guide angle is perfect for mobile, commute-friendly review. I share your concern about over-summarization: to prevent “outsourcing understanding,” I’d use Notebook LM to generate prompts for active recall (flash-style questions) and self-explanations, then do answers without looking before checking the sources.
Another caution is the Dunning–Kruger effect. Shallow summaries can create a false sense of mastery: students feel accomplished because the material seems clear and simple, but this satisfaction can prevent them from seeking deeper understanding or doing additional research. Without checks for depth of comprehension, it’s easy to stop at “I get it” rather than engaging critically.
Privacy is the other big flag. Your note about “better data protection” being pay-gated is important. A practical stance for classrooms: stick to course materials only, avoid sensitive personal notes, export local copies, and (when possible) prefer on-device workflows. I’d love to see a mini-pilot where students compare: raw readings vs. LM-guided recall over 2 weeks—tracking confidence, accuracy, and time-on-task.