Note: This is the second half of the final part of a five-part series exploring how artificial intelligence tools might be effectively integrated into the AVID Focused Note-Taking Process.
Step 5 of AVID’s focused note-taking process focuses on applying learning. Part of that application includes applying learning to real-world problems and contexts.
You’ve likely had students ask the question, “When am I ever going to use this in the real world?” Students ask this because they are looking for relevance and a reason to invest in the learning.
Students are not the only ones who should be thinking this way. As teachers, we can benefit by beginning our lesson and unit planning with the question, “When and how will someone use this in the real world some day?”
The answers to those questions can guide our planning in a way that makes learning more authentic and meaningful.
Applying Learning
In the Applying Learning phase, students use their notes. More specifically, they save and revisit those notes, using them as a resource or learning tool to help apply or demonstrate what they have learned. This new learning can be used to solve real-world problems and address authentic audiences.
Integrating AI
Let’s take a look at seven ways that students might partner with AI to apply their learning to real-world contexts. Some of these scenarios will include content-specific examples to illustrate how the strategy can work. You’ll likely need to reimagine these examples using content that aligns to your assigned learning standards. The examples provided here are intended to be idea starters, not an exhaustive list.
1. 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 to 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 _____. Give me a realistic situation where this matters for _____. I will solve it using my notes.” Notice how that 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 to construct a solution, using knowledge of the local community and details drawn from their notes to guide them.
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.
2. Design for a Real Audience
Identifying a real audience moves the learning from the textbook to the community. It is 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 this audience either in person, virtually, or through a report or other type of asynchronous communication.
Addressing a real audience can be motivating, though it can also be intimidating because students may not have a lot of experience in that setting. AI can be of assistance in lowering that level of uncertainty, making the challenge more manageable.
Students can ask the AI questions about their target audience in relation to the problem or topic they’ve identified. If students are going to present to the school board, they could ask the AI, “What questions might a K–12 school board have about my topic?” The AI’s response can help shape the research plan and give students direction.
If you want to keep the process closer to the classroom, you can have students teach their new learning to students who are younger or older than them. To help with this, students can ask the AI, “In what ways might I need to adapt the information in my notes to my audience so that it makes sense and connects with them?” Students would then add a description of their target audience before submitting the prompt.
In both of these scenarios, the students are using AI as a communication coach to fill in gaps. Students are still doing the heavy lifting, crafting the content, and designing an appropriate way to reach their audience.
3. Career Connection Task
For this approach, students connect their new learning to a profession. Ideally, they identify a profession in which they are genuinely interested and one that has high relevance to their focus of study.
To find a connection to the content, they can prompt AI for ideas. For instance, a geometry student might enter a prompt like, “How do architects use angle and area concepts in real projects?” The student could then follow up with, “Develop a mock design challenge that I can complete to apply my geometry in a realistic manner.”
Upon generating a challenge task that piques their interest, students would then complete it. 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 personalized task to be completed.
This approach works because AI is helping students develop a challenging task that has relevance to both their prior learning and their interests. The students still must do the hard work of completing the task. Real and extended learning can happen in this type of authentic application of course content.
4. Conversation Simulation
This strategy is appealing if you would like to extend student thinking but also want to keep things more manageable and confined to your classroom.
With a conversation simulation, students use their notes to engage in a conversation with a virtual expert or a personality from another time or location. An English student might converse with William Shakespeare. A science student might speak with an astronaut. A history student might make choices and decisions as if they were living in another time period.
Let’s take a closer look at the history example. For this one, after taking 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, an example of which is provided below. Since this is a longer prompt, it’s probably a good idea to provide it to the students so that they can paste it into their own chatbots. That way, they can focus on the learning and not worry about the prompt writing or retyping it accurately. Here’s the prompt:
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. Here are some example questions:
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- 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.
After that, evaluate my reasoning for historical accuracy, strength of evidence, and whether I considered multiple perspectives. Suggest improvements without rewriting my work.
With this being a fairly complex prompt, you can adjust it as you see fit. Additionally, it’s often helpful to ask the AI to write the prompt for you. To get something like the previous history example, you could describe a general idea for the American History shop owner simulation. With that context in mind, you can enter a summary of the idea with a prompt like, “Generate a sample prompt for this where the AI prompts the student through the decision-making and notes-application process.”
This approach turns AI into a role-playing bot that generates realistic scenarios and then prompts the student through complex thinking challenges. The AI is facilitating, while the student is doing all the answering, problem-solving, and thinking.
5. Public Awareness Campaign
This approach can work with nearly any subject matter, and it requires students to communicate their learning in an effective way to either inform or persuade a target audience. Oftentimes, persuasion requires a higher degree of critical thinking, but informative communication works as well.
AI can be used in several ways here. It can be used to help identify potential misconceptions that audience members may have about the topic area. For instance, a science student might prompt: “What misconceptions do people often have about recycling or energy use?”
Another student might tell a chatbot their point of view on a controversial subject and then ask, “What are some reasons people may disagree with me on this?” The response then gives the students targets for their persuasion campaigns.
Once the student has a message and some insights about their audience, they can create the campaign. They might even use AI to help design visuals to incorporate into a presentation or handout. The AI-assisted visual aid creation process requires very clear directions and descriptions, which is an important skill for students to have.
Here, 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 audience.
6. Interview an Expert Role Play
This can be approached in two different ways. In one scenario, students can tell the AI to be an expert on a topic that they are studying and then ask questions to be answered around that topic area.
You could also flip this around and have the student be the expert. This makes sense since they just learned and took notes on the content to be discussed. For this approach, students might enter a prompt such as, “I’m a high school biology student studying Charles Darwin and the biomes of the Galápagos 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 also want to require me to think deeply about what I’ve learned.”
With this approach, students are engaging in dialogue about the topic. The probing questions can deepen understanding. The experience is also very engaging and interactive.
7. Build Something Better
For this one, students strive to improve an existing system using content knowledge anchored in their notes.
To do this, they first identify a need. What needs to be better? You can control this part of the process to the degree you see fit. You can have each student develop their own improvement target, you could brainstorm as a class and all have the same goal, or you could have students work in groups on common goals.
Students use their in-class knowledge to solve the related problem and then leverage AI to test those ideas.
To do that, students would enter a prompt that describes the problem they see before offering their solution. To get productive feedback, they’d add on to the prompt: “Analyze my target problem and my proposed solution. Identify and list strengths and weaknesses.” Based on the feedback they receive, students get to work improving their plan.
Another approach would be for them 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.”
The value here is that students are engaging in authentic problem-solving and using the AI to push and challenge their thinking in order to make their solution more effective.
For any of these approaches, a good sequence of actions for students to follow is Learn → Notes → Apply → AI Support → Revise → Share. Some of these steps will likely be repeated, but this is the general progression.
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.
When students use notes only to study, learning stays small. When they use notes to solve real problems, create for real audiences, and improve real systems, learning becomes powerful.
AVID Connections
This resource connects with the following components of the AVID College and Career Readiness Framework:
- Instruction
- Rigorous Academic Preparedness
- Opportunity Knowledge
- Student Agency
- Insist on Rigor
- Break Down Barriers
- Align the Work
- Advocate for Students
Extend Your Learning
- The Five Phases of the Focused Note-Taking Process (AVID)
- ChatGPT (OpenAI)
- Claude (Anthropic)
- Gemini (Google)
- Copilot (Microsoft)
- NotebookLM (Google)