Applying AI Fluency to learning materials and assignments

Leveraging AI Fluency to Create Learning Materials

Let's explore using AI fluency to create materials your students actually work with—everything from tomorrow's worksheet to a final exam. Learning aids can help bridge the gap between what students can do independently and what they can do with the knowledge acquired in your course. Whether it's a worksheet to complement a lecture, an activity for student practice, or a graded assignment, we spend countless hours trying to create materials that actually help our students learn.

AI can be a powerful connector to help you better understand and build that bridge for your students. With context from past conversations, uploaded files, and connected tools, every worksheet, activity, and assessment you create can now draw on a much deeper understanding. Your AI partner knows your specific learning objectives, how they relate to your chosen content, the pedagogical journey you've mapped for your students, and your teaching philosophy and constraints. This means you're never starting from scratch; instead, you're creating materials that build logically on whatever came before.

The Four Ds Framework in Material Creation

As before, we'll be working through all "Four Ds" from the framework. In this section, we keep things fairly high-level and focus on the general dynamics of working with AI in this way.

1. Delegation: Identifying the Need

Let's say a group of journalism students are struggling with a concept, like understanding null and alternative hypotheses in statistics, especially when variables aren't obvious. You need an assignment that addresses their confusion.

Thanks to your previous context building, your AI partner already knows:

• This is a part of your Week 4 learning objectives.
• Your journalism students struggle with mathematical abstractions.
• You've been building towards hypothesis testing through real-world examples.
• Previous activities used social media analytics as a bridge concept.

In this situation, your problem awareness tells you why your students are confused and what needs to be addressed. Your platform awareness reminds you that AI can quickly generate diverse practice problems. But most importantly, your established context means AI can generate problems that fit perfectly into your pedagogical sequence. Task delegation becomes natural: you define the specific confusion point and desired outcome, and the AI creates varied practice scenarios that align with your course's progression.

2. Description: Sharing Your Vision

When it comes to building learning materials, context is key. Instead of starting fresh, you could reference a shared understanding by telling the AI: "Remember how we discussed that my journalism students need concrete examples before abstractions? Let's create a worksheet for null hypotheses that builds on the social media analysis examples from last week."

Then, add specific details with rich descriptions of product, process, and performance:

• Format: "Format as a worksheet with clear numbering; include space for student work; put the answer key on a separate page."
• Process Expectations: "First, target their main misconceptions about identifying variables. Then, start each problem with a relatable scenario from their daily lives. Next, ask students to identify variables before writing hypotheses. Build complexity gradually."
• Performance Parameters: "Be creative with scenarios, but keep language at a 10th-grade reading level. If any problem seems too complex, flag it for me."

3. Discernment: Applying Your Expertise

As AI generates options, apply your discernment skills to identify what is useful. For example, if the AI suggests an assignment using a popular social media app, and you find it perfect because it's accessible and relevant, don't just move on. Explain to the AI why it works to get better suggestions in the future.

Conversely, if a suggestion is a bad fit, explain why rather than just rejecting it. This back-and-forth of describing and discerning keeps the context growing. Along the way, you might discover new ways to teach topics or explore angles you hadn't considered. Good discernment recognizes both problems and possibilities—sometimes AI suggests an approach from another field that brilliantly illuminates your concept.

4. Diligence: Intentionality and Responsibility

Diligence means being intentional in your collaboration:

• Creation Diligence: Protect sensitive data and model responsible AI use. Pay careful attention to academic citations.
• Transparency Diligence: Use your own AI use as a teaching opportunity. Write diligence statements and discuss them with students so they understand the role of your human expertise.
• Deployment Diligence: Review materials carefully for accuracy, bias, errors, and alignment with objectives. Ultimately, you own the impact of the materials you create.

Designing a Cohesive Learning Experience

As an AI-fluent educator, you don't need a "secret prompt" for every study guide, lab, rubric, or exam. You just need to recognize how to apply your existing teaching and subject matter expertise to your AI collaborations. The more you work with AI, the easier it becomes to create materials that work together seamlessly. You might ask the AI:

• How does this lab connect to the worksheet?
• What preparation have students already had?
• Where does this lead in the curriculum?
• What level of support do students need?

Your materials should feel intentional and designed as a cohesive learning experience, not a collection of random activities.

Addressing the Challenge of AI in Education

The fact that AI can complete many assignments at a high level of competency creates a massive challenge for education. Each context, from kindergarten to graduate school, faces unique obstacles in assessing student learning. We are all figuring out how to teach ethically and effectively in an AI-infused world.

AI fluency can help us reckon with this challenge. To exercise diligence, we need to first elucidate our concerns and standards with colleagues and students. These conversations can directly inform the process of collaborating with AI to design assignments and assessments.

Summary of Key Strategies

1. Build Context through Conversation: Start simple and add complexity. When AI drafts something too advanced, explain the confusion and ask for scaffolding. When it gives you something amazing, explain why it’s good to improve future output.
2. View Everything Through Your Teaching Lens: Every AI suggestion needs your expertise. Does it match your learning objectives? Your expertise transforms generic content into targeted learning experiences.
3. Practice Real Diligence: Verify accuracy, check for unintended biases, and document your collaboration process to model responsible interaction for your students.

This approach is not just about creating materials faster; it's about creating materials that work better because they emerge from a genuine collaboration between your pedagogical expertise and AI's capabilities. The result is a coherent learning journey where assessments align with actual teaching and resources embody your unique pedagogical vision.