AI Collaborative Toolkit

[Kristal]

Last school year, Leading Educators and FullScale launched the School Teams AI Collaborative, which brought together ~80 innovative educators who were implementing AI in their instruction. We learned a ton and we are excited to share two resources that capture learnings from the Collaborative:

• Problems of Practice Series: We captured AI-enabled strategies and compiled them into categories. This series is designed to guide leaders and educators in using generative AI powerfully, equitably, and responsibly by showcasing the strategy, providing artifacts from Collaborative members, and providing guidance on how others can apply it in their school. Each tackles a unique, timely challenge in education:
  • How can school and district leaders build the foundation for effective, equitable AI use?
  • How can teachers enhance teaching and learning with generative AI?
  • How can teachers help students use generative AI responsibly and meaningfully in their learning?
  • Will flag for you there's a strategy focused on NotebookLM
•  Report: Generative Practice- Practical Insights for Unlocking the Instructional Potential of AI from the School Teams AI Collaborative provides a synthesis of what educator needs around AI that the field most urgently faces and recommendations for policy leaders, school system leaders, and other key stakeholders.

Asks: 

• Feedback: We'd love to hear your reactions. What resonated with you? Was there something new you hadn't seen before? Are there ways to strengthen the resources?
• Share the Work: This dissemination toolkit has graphics and suggested text. We'd be grateful if you would share them on social media and with your networks.
• Opportunities to Partner: Leading Educators and FullScale continue to work around AI to make sure the technology is being used in a responsible, safe, and effective way that is pedagogically sound. If you see opportunities for us to partner, we'd be happy to discuss them.

[Peerapong]

For my thoughts. I think the efficiencies in use and functionality for instruction are a lot in the AI you create. However, students today concentrate very little on their studies due to the abundance of online media and social media. Entertainment that students are interested in is more than liking learning and may use AI to commit crimes. Laws, regulations, including socializing.

[lcswater]

【✨ 升級你的自主學習魂！NotebookLM 三大隱藏神技，老師沒教的終極用法大公開！】

嗨，大家好！👨‍💻 各位資訊科技AI達人，是不是覺得 AI 筆記工具 NotebookLM 只是個普通的資料整理神器？那你就太小看它了！今天，我要揭開它三大生成功能中，那些課本上沒寫、卻能讓你學習效率指數級飆升的「隱藏版」神秘用法，保證讓你大開眼界！

📚 功能一：文件問答 (Chat with your documents)

大家可能都知道可以上傳課本內容，然後問它問題。但真正的神用法，是把它變成你的「專屬出題老師」！

🧠 神秘用法：反向提問，打造個人化題庫！

與其問「第二次世界大戰什麼時候開始？」，不如試著對它下指令：

➡️ 指令範例：「請你根據我提供的第二次世界大戰章節，設計 5 道選擇題，並附上正確答案與詳解。」

哇！瞬間，NotebookLM 就會化身為你的家教，針對你上傳的任何科目內容，幫你產生無限的練習題。從此告別題庫書，考前複習超給力！

💡 功能二：筆記摘要與洞察 (Summarize & Get Insights)

把一堆資料丟給它，自動生成摘要很基本。但你知道它能扮演「學習策略分析師」嗎？

🧠 神秘用法：跨文件主題連結，抓出核心概念！

當你在做科展或專題報告，需要閱讀大量資料時，試試這個：

➡️ 指令範例：「我上傳了三篇關於「AI 倫理」的文章，請幫我比較這三篇文章的主要論點差異，並找出它們共同的關注點。」

NotebookLM 不僅會分開摘要，更會像偵探一樣，幫你找出不同資料間的關聯與矛盾之處，讓你的報告或專題，立刻擁有超越同齡人的深度與廣度！

✍️ 功能三：創意寫作輔助 (Creative Writing Assistant)

請它幫你寫作文草稿？太普通了！讓它成為你的「程式碼除錯與優化教練」吧！

🧠 神秘用法：貼上程式碼或截圖，進行白話文解說與除錯！

國中開始學程式 (例如：Scratch, app inventor2、Python)，卡關是家常便飯。這時候，把你的程式碼片段或截圖貼給它(記得NotebookLM僅支援以下檔案類型：pdf, txt, md, 3g2, 3gp, aac, aif, aifc, aiff, amr, au, avi, cda, m4a, mid, mp3, mp4, mpeg, ogg, opus, ra, ram, snd, wav, wma//所以秘技就是將Scratch, app inventor2 的截圖圖片檔，貼至google文件，再上傳此文件資源，勾選此來源，再輸入下方指令範例即可)：

➡️ 指令範例：「這是我寫的一段 Python 程式碼，它一直出現錯誤。請用國中生聽得懂的方式，解釋我哪裡寫錯了，並提供修改建議。」

或

➡️ 指令範例： 請解釋來源資料的程式碼?並告訴我該如何修正?

它會像個超有耐心的助教，一步步帶你理解程式邏輯，找出 Bug，甚至還會教你更簡潔的寫法。可以更快培養獨立思考解決問題的能力！

總結來說，別再把 NotebookLM 當成一個被動的資料庫了！透過下達更精準、更有創意的指令，你就能解鎖它的隱藏潛力，把它變成一個全天候的個人化家教、研究助理兼程式教練。🚀

除了這三招，你還發現過 NotebookLM 的哪些「神操作」嗎？或者，你最想試試看以上哪個功能來解決你目前的教學或學習難題呢？在底下留言交流一下吧！👇

#NotebookLM #AI學習 #資訊科技 #國中生必學 #自主學習

我在fb上的分享 

[DoctorHarves]

After reading the report, several themes stood out strongly to me. What resonated most was the idea that educators must be treated as co-designers rather than passive implementers of AI. Too often in schools, new tools are dropped in from above without proper consideration of how they align with pedagogy. The report’s insistence on collective school teams having the time, trust, and agency to experiment with AI mirrors my own experience that innovation is only sustainable when it is collaborative and grounded in classroom practice. I also found the “Hop, Skip, Leapfrog” framework particularly powerful. It gives us a shared language for situating our work, showing that small, incremental “hops” are not failures but signs of progress on the path toward meaningful transformation. The concrete case studies, from AI-generated feedback protocols to multilingual support chatbots, further highlighted that AI is not about shiny tools, but about pedagogy that centres student agency.

One aspect of the report that was new to me was the framing of policy as a lever for instructional transformation. I’ve seen policy discussed primarily as a compliance measure, but here it is positioned as something that can actively signal priorities, unlock innovation, and build trust. That perspective feels directly applicable to New Zealand, where much of the AI discussion is still framed around restriction and risk. Another fresh idea was the call for rapid, context-aware research cycles led by practitioners. Traditional research often lags behind classroom realities, but the recognition that teachers themselves can generate short-cycle, design-based research validates grassroots innovation and offers a more responsive model.

At the same time, I see areas where the resources could be strengthened. While student agency was acknowledged, the report was largely framed around educator and system voices. Including more student reflections and opportunities for co-design would have highlighted the fact that AI transformation is ultimately about learners. I would also like to see greater emphasis on equity in cultural contexts. In Aotearoa New Zealand, for instance, Te Ao Māori perspectives could add richness and ensure that AI adoption reflects diverse worldviews. In addition, while the VATT and HSL frameworks are strong, schools will need practical, teacher-friendly tools such as rubrics, reflection prompts, or decision trees that can be used without adding to workload. Finally, the discussion of pilots could be extended by making clearer connections to system-wide priorities, such as New Zealand’s NCEA reforms or the digital curriculum, so that small-scale experiments can build toward broader impact.

Overall, what I valued most in the report was its balance between realism and aspiration. It acknowledges that most schools are still at the “hop” stage, but it frames this not as a weakness but as a signal that meaningful learning and adaptation are happening. At the same time, it paints a clear path toward leap-level transformation if systems and policies genuinely support practitioner-led innovation. It is this combination of grounded practice and future vision that makes the report both practical and inspiring.