In this week’s update, we do something different: instead of racing through a dozen new AI releases, we go deep on a single tool that is quietly becoming one of the most powerful platforms for academic work—Notebook LM.Built on Google’s Gemini models, Notebook LM acts as a virtual research assistant, teaching assistant, and study companion all in one. We walk through how it reduces hallucinations by grounding responses in your own sources, how the interface is structured (sources, chat, and studio), and why its mind maps, audio overviews, and quizzes are particularly well suited for higher education.Along the way, we show concrete use cases: building literature reviews, designing courses, supporting collaborative student projects, and improving accessibility with multimodal overviews and multiple languages. We also look ahead to deep research and how combining serious search with structured notebooks could change how faculty and students approach scholarship.If you’re an instructor, instructional designer, or student wondering how to move beyond “just chatbots” and toward more active, source-grounded work with AI, this walkthrough of Notebook LM is for you.Chapters00:00 – Welcome & why a deep diveWeekly update format change and why Notebook LM deserves a full episode.01:00 – What is Notebook LM? Hallucinations rethoughtGoogle Labs background, Gemini under the hood, and how Notebook LM changes the type of hallucinations you see.03:10 – Free vs. paid, source limits, and supported content50 vs. 300 sources, YouTube transcripts, PDFs, web links, Google Drive, and what “non-multimodal” means in practice.05:20 – Inside a featured notebook (The Economist example)Exploring featured notebooks from outlets like The Atlantic, Our World in Data, and The Economist’s “World Ahead 2025.”09:10 – The Studio: mind maps, chat, and grounded citationsUsing mind maps for topic exploration, hovering over citations, and teaching students to verify sources directly.13:40 – Audio & video overviews as tutorsHow automatically generated “AI podcasters” summarize your sources and the new interactive audio mode for asking follow-up questions.16:45 – Flashcards, quizzes, and real student study workflowsGenerating flashcards and quizzes, explanation features, and how students are actually using these for exam prep.18:45 – Building a notebook from scratchCreating a new notebook with web pages, YouTube transcripts, Google Drive docs, and Discover for additional web/Drive sources.24:50 – Reports, study guides, and writing supportUsing built-in templates for study guides, briefing documents, research proposals, and blog posts.26:30 – Teaching, research, and accessibility use casesCourse design, collaborative research notebooks, lecture transcripts, and accessibility gains through audio, visuals, and languages.32:00 – Sharing, collaboration, and analyticsNotebook-level sharing, object-level sharing (just an audio overview or notebook), permissions, and usage analytics.35:10 – Are we still thinking? Rethinking learning with AIMind maps, interactive audio, and group notebooks as ways to deepen—not replace—student thinking.37:25 – Deep research and the future of Notebook LMHow deep research could supercharge notebooks with high-quality academic sources and what that means for the free tier.39:50 – Final thoughts & call for use casesWhy Notebook LM stands out today and an invitation for listeners to share their own experiments.#aixhigheredpodcast #notebooklm #GoogleGemini#AIinEducation#HigherEdAI#GenerativeAI#EdTech#TeachingAndLearning#AIforTeachers#AIinHigherEd