#499 – Using AI to Enhance Focused Note-Taking, Step 5b: Applying Learning to the Real World

#499 — Using AI to Enhance Focused Note-Taking, Step 5b: Applying Learning to the Real World

AVID Open Access
14 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 5b: Applying Learning to the Real World. In the last episode of Tech Talk for Teachers, we began exploring the final step of AVID’s focused note-taking process: applying learning. In that episode, we focused on applying new learning from the note-taking process to new thinking. In today’s episode, we’re going to continue that focus by extending the new learning to real-world applications.

This is where learning becomes the most meaningful. You’ve probably all had students ask the question, “When am I ever going to use this in the real world?” Students are looking for relevance and a reason to invest in the learning. Students are not the only ones who should be thinking this way. I think as teachers, we should all begin our lesson and unit planning with the question, “When and how will someone use this in the real world someday?”

The answer to that question, like the answer to the students’ questions, should guide our planning. We should be striving to have students apply their learning in the most meaningful way possible. This step in the focused note-taking process gives us the perfect opportunity to answer those questions.

To get us started in the right direction, let’s take a look at seven ways that we can have students apply their new learning to real scenarios, while also infusing the use of AI into the process. For some of these scenarios, I’ll use specific content examples to illustrate how it can work. You’ll want to reimagine these examples using content that aligns with your learning standards as you listen. Keep in mind that these are examples and idea starters. It’s not an exhaustive list.

Transition Music with Rena’s Children 1:51 Here is your list of tips.

Paul Beckermann 1:55 Number one: community problem solver. This approach connects students to problems of local relevance. They should examine their own community and identify a local problem that relates to their new learning. If they’re unsure what problems may exist, they can use AI for help. For instance, if they live in the Midwest and have recently learned about water pollution, they might ask AI, “What are common water quality concerns in Midwestern communities?”

Another option is to give students a more general sentence stem. They could use something like, “I learned about [blank]. Give me a realistic situation where this matters for [blank]. I will solve it using my notes.” Notice how the last part tells the AI not to do the solving, just to frame the scenario. Once students have identified a problem, they then conduct their own research, using their knowledge of the local community and details drawn from their notes to guide them. This all works because, while AI is helping to identify a potential problem to solve, the student is doing the research, drawing upon new learning, and developing a potential solution. The student is doing the most difficult tasks in this process.

Number two: design for a real audience. Identifying a real audience moves the learning from the textbook to the community. It’s one of the most powerful ways to make learning real, and it’s a great way to have students apply their learning authentically. In the previous community problem solver example, an authentic audience could have been added, such as a school board, city council, or community group.

Students could actually present their ideas to these audiences, either live in person, live virtually, or through a report or other type of asynchronous communication. Addressing a real audience can be motivating. It can also be intimidating because the students may not have a lot of experience doing this. AI can be of assistance in lowering that level of uncertainty, making the challenge more manageable.

Students can ask AI questions about their target audience in relation to the problem or topic they’ve identified. If students are going to present to a school board, they might ask AI, “What questions might a K-12 school board have about my topic?” The response can help shape the student’s research plan and give them direction.

If you want to keep the process closer to the classroom, you might have students teach their new learning to students who are younger or maybe older than them. To help with this, students can ask AI, “In what ways might I need to adapt the information in my notes to my audience so it makes sense and connects with them?” Students would then describe their target audience. In both these scenarios, students are using AI as a communication coach to fill in gaps. It works because the students still need to do the heavy lifting, craft the content, and design an appropriate way to reach their audience.

Number three: career connection task. For this approach, students connect their new learning to a profession. Ideally, they identify a profession in which they’re genuinely interested and one that has a high relevance to their content of study. To find a connection to the content, they can prompt AI for ideas. For instance, a geometry student might enter a prompt such as, “How do architects use angle and area concepts in real projects?”

The students can then take it a step further and ask AI, “Develop a mock design challenge that I can complete to apply my geometry in a realistic manner.” Once students generate challenge tasks that pique their interest, they would actually complete those. For instance, they might be prompted to create a blueprint sketch with calculations for a new sports facility on campus. That’s their project. That’s their task.

This approach works because AI is helping students develop a challenging task that has relevance to both prior learning and their interests. The students still must do the hard work of completing the task. Real and extended learning can happen in that authentic application of course content.

Number four: conversation simulation. This strategy is appealing if you want to extend student thinking, but you also want to keep things more manageable and, again, confined to your classroom. With a conversation simulation, students use their notes to engage in a conversation with an expert or a personality from another time or location.

An English student might converse with Shakespeare, a science student might speak with an astronaut, or a history student might make choices and decisions as if they were living in another specific time period. Let’s take a closer look at the history example and how AI plays into this. For this one, after notes on the American Revolution, students could imagine themselves as a colonial shop owner.

With this type of application, a more sophisticated AI prompt may be needed than usual. Here’s an example of one that could be used, and since it’s longer, it’s probably a good idea to give it to the students so that they can paste it into their own chatbots, rather than retyping it all. That way, they can focus on the learning and not worry about the prompt writing. So here it is:

“I am learning about the American Revolution and have notes on British taxes, colonial protests, boycotts, the Intolerable Acts, loyalists, and patriots. I want you to act as a historical simulation coach, not someone who gives me answers. Create a realistic scenario where I am a colonial shop owner in Boston in 1774. Describe my situation, challenges, and pressures I would face. Then guide me through the decision-making process one step at a time by asking me questions that require me to use evidence from my notes. For example: What groups might influence your decision? What economic risks do you face? What values or beliefs matter here? What might happen if you support a boycott? What might happen if you remain loyal to Britain? After each response I give, ask a deeper follow-up question. Do not decide for me. Do not write my final answer. When I am ready, ask me to make a final decision and justify it with evidence from my notes. Then evaluate my reasoning for historical accuracy, strength of evidence, and whether I considered multiple perspectives. Suggest improvements without rewriting my work.”

All right, there you have it. It’s a fairly complex prompt and pretty long, and you can adjust it as you see fit. But that’s the idea. Other simulations can be designed for other content areas as well. In fact, I find that it’s helpful to ask AI to write the prompt for you. To get the example I just shared with you, I was describing the American history shop owner simulation I had in mind. Once the AI had that context, I entered the prompt: “Generate a sample prompt for this where the AI prompts the student through the decision-making and notes application process.” This all works because it turns AI into a role-playing bot that generates realistic scenarios and then prompts the student through complex thinking challenges. The student is doing all the answering, problem-solving, and thinking; the AI is facilitating.

Number five: public awareness campaign. This approach can work with nearly any subject matter. It requires students to communicate their learning in an effective way to a target audience, to either inform or persuade. Oftentimes, persuasion requires a higher degree of critical thinking, so I often lean that way, but informative communication works really well too.

AI can be used in several ways here. It can be used to help identify misconceptions people have about a topic area. For instance, a science student might prompt, “What misconceptions do people often have about recycling or energy use?” Or a student might tell a chatbot their point of view on a controversial subject and then ask the AI, “What are some reasons people may disagree with me on this?” That response gives students ideas of what to prepare for and targets for the persuasion campaign.

Once the student has the message and some insights about their audience, they can create their campaign. They might even use AI to help design visuals to incorporate into it. The visual aid creation process requires very clear directions and descriptions, and this is an important skill for students to have. This approach works because AI acts as an advisor while the students must do the hard work of creating the message, approach, and actual communication content. Ideally, this is an authentic communication task targeted toward an authentic, or at least authentically simulated, audience.

Number six: interview and expert role-play. This can be approached two different ways. In one scenario, students can tell the AI to be an expert on a topic they’re studying, then they can tell the AI to act as that expert in answering the students’ questions. You can also flip this around and have the student be the expert.

This makes a lot of sense, since they just learned and took notes on the content to be discussed. So for this approach, students might enter a prompt such as, “I’m a high school biology student studying Charles Darwin and biomes of the Galapagos Islands. Ask me questions as if you were a reporter for an English newspaper wanting to learn more. Make your questions friendly but probing. You want to learn more and you want to require me to think deeply about what I’ve learned.” This works because students are engaging in dialogue about their topic. The probing questions can deepen understanding. The experience is also very engaging and interactive.

Number seven: build something better. For this one, have students improve an existing system using content knowledge anchored from their notes. To do this, they identify a need: what needs to be better? You can control this part of the process to the degree that you see fit. You could have each student develop their own improvement target, you could brainstorm as a class and have all of them use the same goal, or you could have students work in groups on common goals.

There are many approaches; you choose what works for you. Students then use their in-class knowledge to solve the related problem and then use AI to test those ideas. To do that, students would enter a prompt that describes the problem they see, and then they’d offer their solution. To get productive feedback, they’d add on to the prompt something like: “Analyze my target problem and my proposed solution. Identify and list strengths and weaknesses.”

Based on that feedback, students could get to work improving their plan. Another approach would be to ask the AI: “Ask me three questions that challenge me on my solution. Your questions should be insightful and help me strengthen my plan.” This works because students are engaged in authentic problem solving and using AI to push and challenge their thinking in order to make their final solution more effective.

Paul Beckermann 12:24 For any of these approaches, a good sequence of actions for students to follow is: learn, then go to notes, then apply, then get some AI support, then revise the work, and then share. Of course, some of these steps can be repeated, but it’s a general idea. Throughout the process, AI should be used to help generate scenarios, audiences, questions, constraints, or feedback.

Students should provide the ideas, evidence, decisions, explanations, and final products. In general, when students use notes only to study, learning stays small. When students use notes to solve real problems, create for real audiences, and improve real systems, learning becomes powerful. Let’s make learning powerful.

Paul Beckermann 13:13 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. Thanks for all you do. You make a difference.