#497 – Using AI to Enhance Focused Note-Taking, Step 5a: Applying Learning to New Thinking

#497 — Using AI to Enhance Focused Note-Taking, Step 5a: Applying Learning to New Thinking

AVID Open Access
10 min

Transcript

The following transcript was automatically generated from the podcast audio by generative artificial intelligence.  Because of the automated nature of the process, this transcript may include unintended transcription and mechanical errors.

Paul Beckermann 0:00
Welcome to Tech Talk for Teachers. I’m your host, Paul Beckermann.

Transition Music with Rena’s Children 0:05
Check it out. Check it out. Check it out. What’s in the toolkit? Check it out.

Paul Beckermann 0:16
The topic of today’s episode is Using AI to Enhance Focused Note-Taking, Step Five A: Applying Learning to New Thinking. We’ve come to the final step in AVID’s focused note-taking process. Up to this point, students have taken notes, processed them, connected notes to thinking, and both summarized and reflected on their learning.

Step five is applying learning. In this step, students use their notes as a resource or learning tool to help them apply or demonstrate what they’ve learned. It sounds simple, almost an afterthought to the note-taking work that’s already been done. However, I’d argue that step five is both the most complex and the most important of the five parts in the process. It’s where the work and learning students have done can finally be applied to real applications and real problem-solving. It’s where the true purpose of that information comes to fruition—where it really matters beyond recall or an answer on a test.

In fact, this step of the process is so expansive and open to so many possibilities that I’m going to break it down into two episodes. Today, I’ll focus on extending and deepening understanding of the notes. Next time, we’ll dig into using this new depth of understanding to solve problems, create something new, and perform for authentic audiences. With this context in mind, let’s take a look at eight ways students can apply their learning in order to extend their understanding of the content. This process will allow them to stretch, question, and connect, setting them up to be better able to apply their new learning at high levels.

Transition Music with Rena’s Children 1:52
Here is your list of tips.

Paul Beckermann 1:56
Number one: Explaining without notes. With this approach, students close their notes and explain concepts in their notes from memory. They can use an audio recording to capture their ideas, or they can write them down. Once they have their summary, they enter it into an AI chatbot along with a prompt like, “Based on this summary and explanation, what key ideas may I be missing?”. Students would then revise their explanation based on that feedback provided by the AI. This works because students are required to practice retrieval and make meaning before getting feedback from the AI.

Number two: AI Question Coach. In this strategy, students use AI to push them into asking tougher questions. For this to work, the teacher would provide some initial probing questions for students to answer based on the notes. Students enter these questions and the responses into the AI and then ask, “Ask me three tougher questions about this topic to stretch my thinking”. Students then record those questions and respond. You could have them answer all of them, or choose one or two of the three. Still another approach would be to compile all the AI ideas as a whole class and then discuss and choose together. This works because AI questions push the students further by increasing the rigor of the questions. Students are still doing the heavy lifting and deep thinking to answer the questions.

Number three: Real-world examples. This scenario sets students up for what will come next—real-world applications—but it gives them a voice and ownership in the process. Students begin this process by brainstorming real-world applications on their own. Where do they see this new learning being relevant beyond their classroom? How might it be used to solve a problem?. Students could then enter these ideas into an AI chatbot and then prompt, “Give me three additional real-world situations where this concept matters”. Again, students can compile these ideas as a class or work independently. In either case, they would compare their own ideas with the AI suggestions, explain which examples are strongest, and choose one to pursue further. This works because it requires students to do their own brainstorming first, evaluate AI suggestions, and then scaffold goal setting for future learning and application.

Number four: Cross-subject connections. This is similar to generating real-world examples, but it keeps the focus more academic and encourages students to make connections between school-related subjects. To begin this process, the teacher asks students, “How does what you have learned matter in other classes you are taking or have taken?”. Students then write down their explanations using their notes as a reference; they should provide examples to support their thinking. Then they enter this work into AI and ask, “What are additional connections I could make between subjects A and B?”. A represents the content from their notes, and B is the additional subject matter to which they are connecting. Students review the feedback from the AI and add any missing ideas to their original brainstorm. This works because students are leaning on AI to help extend upon their original work, while the students still must engage in the original academic thinking.

Number five: Build study tools from notes. Here, students provide the notes to the AI and ask it to generate study aids like flashcards, quiz questions, vocabulary reviews, and practice prompts. NotebookLM is particularly useful for this. Students then use these aids to practice and strengthen their understanding of the content in the notes. As they do this, they should review the review tools for potential errors. AI can hallucinate, after all. They can also use the quiz materials to challenge their friends or a partner group. This strategy works because the AI is facilitating review while the students are the ones answering the questions.

Number six: Debate partner. For this approach, students develop a claim based on their notes that can be debated. It could mean taking a position on a controversial issue, or perhaps suggesting a solution to a problem. Students then enter this information into the AI chatbot and prompt it to participate in a friendly debate by arguing the opposite side of the issue: “Use evidence to support your arguments and question any weak evidence I present. Use a friendly tone”. This works because AI is actively engaging the student in debate, prompting higher-level thinking, stretching thinking, and requiring students to defend their ideas with evidence.

Number seven: Error analysis. In this example, the teacher provides a prompt. It can be an open-ended question that challenges students to use their notes to answer or respond to something complex. Students enter the question and their response into the AI, followed by a prompt like, “Review this response. Do not rewrite it. Only identify weak reasoning, missing evidence, or unclear parts”. Students would then make improvements that they feel strengthen their response. This works because the AI is providing feedback while the student does the original thinking as well as the revisions. This approach is particularly helpful when the teacher does not have time to respond personally to every piece of student writing and when timely feedback is important.

Number eight: General use sentence stem. This last approach is a bit of a catch-all. Teachers can give students this sentence stem: “I already used my notes to do [blank]. Now help me improve by [blank]”. This approach works because it keeps AI in the support role, and it also gives students a strategy for approaching future questions and roadblocks that they might face. It can build agency by providing students with a process and a prompt idea ahead of time. This can be particularly effective when using NotebookLM to store student notes. NotebookLM will reference any uploaded notes and resources whenever answering a student question. This keeps the responses focused and relevant.

With any of these approaches, there are a few guardrails a teacher can use to help ensure students, and not the AI, are doing the critical thinking:

1. Require students to use evidence and cite their notes whenever responding to AI.
2. Have students always complete their own draft before turning to AI.
3. Ask AI to coach, not do.
4. Make sure students explain what they change based on AI and why they made that change. They might keep these thoughts in an AI reflection journal.
5. Keep humans in control and make sure students own their final answers and products.

A good way to think about this is HI-AI-HI. HI is human intelligence and AI is artificial intelligence. Humans should come first and last in the process: human intelligence first, then AI input, then human review and decision-making last. As students begin to apply their learning, the strategies presented in this episode can help students begin to deepen and stretch their understanding and mastery of course content. In our next episode, we’ll explore how this deeper mastery of content can be applied beyond the classroom to authentic learning challenges and real-life problem-solving scenarios.

Paul Beckermann 9:15
To learn more about today’s topic and explore other free resources, visit avidopenaccess.org. Specifically, I encourage you to check out the article collection, “AI in the K-12 Classroom,” and, of course, be sure to join us next Wednesday for our full-length podcast, Unpacking Education, where we’re joined by exceptional guests and explore education topics that are important to you. Thanks for listening. Take care, and thanks for all you do. You make a difference.