{"id":867,"date":"2025-11-14T14:41:54","date_gmt":"2025-11-14T14:41:54","guid":{"rendered":"https:\/\/anapoly.co.uk\/labs\/?p=867"},"modified":"2025-11-14T14:47:49","modified_gmt":"2025-11-14T14:47:49","slug":"a-more-personalised-way-to-learn-with-notebooklm","status":"publish","type":"post","link":"https:\/\/anapoly.co.uk\/labs\/a-more-personalised-way-to-learn-with-notebooklm\/","title":{"rendered":"A more personalised way to learn with NotebookLM"},"content":{"rendered":"\n

I came across an interesting piece by AI Maker<\/a> explaining how he uses NotebookLM to learn in a more personalised way. He suggests that learning improves dramatically when we control our sources, shape the content into formats that suit us, and then test ourselves deliberately.<\/p>\n\n\n\n

This approach fits neatly with our existing work and is worth experimenting with. Participants in our labs might appreciate being able to gain confidence with NotebookLM whilst, as a by-product, learning about something else of value to them.<\/p>\n\n\n\n

The approach is outlined below<\/p>\n\n\n\n

\n\n\n\n

Find high\u2011quality sources<\/h2>\n\n\n\n

Learning needs good source material. NotebookLM\u2019s Discover feature helps by scanning the web for material relevant to our topic and filtering out noise. The goal is a clean, reliable starting set.<\/p>\n\n\n\n

We can reinforce that by using Perplexity AI:<\/p>\n\n\n\n

    \n
  1. Use Perplexity\u2019s Deep Research to gather a solid set of articles, videos, documents, and case studies.<\/li>\n\n\n\n
  2. Export the citations as raw links.<\/li>\n\n\n\n
  3. Import those links directly into NotebookLM as our base sources.<\/li>\n\n\n\n
  4. Use NotebookLM’s Discover feature to expand, refine, and diversify the set.<\/li>\n<\/ol>\n\n\n\n

    We should aim for varied perspectives: Reddit for beginner intuition, YouTube for demonstrations, official documentation for depth, enterprise case studies for realism.<\/p>\n\n\n\n

    Build sources into multiple formats<\/h2>\n\n\n\n

    Once our sources are loaded into NotebookLM, we can shape their content into formats to suit how we like to learn.<\/p>\n\n\n\n

    A focused and well-structured report helps us understand faster. Here are three effective techniques:<\/p>\n\n\n\n

      \n
    1. Anchor new ideas to familiar systems.<\/strong> Ask NotebookLM to explain the concept by contrasting it with something you already know.<\/li>\n\n\n\n
    2. Layer complexity progressively.<\/strong> Tell NotebookLM to start with a plain\u2011language explanation, then add the underlying processes, then technical detail.<\/li>\n\n\n\n
    3. Use a structured four\u2011pass model.<\/strong> Request versions for beginner, intermediate, advanced, and expert levels so you can climb the ladder rather than jump in halfway.<\/li>\n<\/ol>\n\n\n\n

      Audio is ideal for learning on the move and for reinforcement. NotebookLM\u2019s podcast generator can be shaped deliberately:<\/p>\n\n\n\n

        \n
      • Beginner interviewing expert<\/strong> for clear explanations and basic intuition.<\/li>\n\n\n\n
      • Expert debate<\/strong> to highlight competing approaches and trade\u2011offs.<\/li>\n\n\n\n
      • Expert critique<\/strong> of the source material to expose over-simplifications or gaps in your understanding.<\/li>\n<\/ul>\n\n\n\n

        Short structured video explainers are helpful for visual learners. We can prompt NotebookLM to create comparison tables, workflows, or mistake\u2011prevention checklists that would be tedious to build ourselves.<\/p>\n\n\n\n

        Test to expose the gaps<\/h2>\n\n\n\n

        NotebookLM\u2019s flashcards and quizzes can help consolidate what has been learnt.<\/p>\n\n\n\n

        \n","protected":false},"excerpt":{"rendered":"

        I came across an interesting piece by AI Maker explaining how he uses NotebookLM to learn in a more personalised way. He suggests that learning improves dramatically when we control our sources, shape the content into formats that suit us, and then test ourselves deliberately. This approach fits neatly with our existing work and is […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[467,83],"class_list":["post-867","post","type-post","status-publish","format-standard","hentry","category-diary","tag-learning","tag-notebooklm"],"_links":{"self":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/posts\/867","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/comments?post=867"}],"version-history":[{"count":6,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/posts\/867\/revisions"}],"predecessor-version":[{"id":873,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/posts\/867\/revisions\/873"}],"wp:attachment":[{"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/media?parent=867"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/categories?post=867"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/anapoly.co.uk\/labs\/wp-json\/wp\/v2\/tags?post=867"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}