Design Brief 2: Rethinking Learning Goals -

With AI, educators are facing a tension I like to call the convergence of messy and the magical. While AI promises exciting innovation and efficiency, it also raises significant concerns, including academic integrity, student privacy, and inclusion.

🎙️ Podcast Focus

In Season 1, Episode 2 of the AI for Educators Design Lab podcast, we move to an issue related to the assignment design question from Episode 1 and ask something more fundamental: If AI can now complete many of the visible tasks our assignments were built around, what does that reveal about the learning goals underneath them?

In the episode, AI is framed as an unintended audit of our educational aims and practices. It is revealing not just which assignments are vulnerable, but which learning goals may no longer be sufficient. Many legacy learning goals were written for a time when education placed much more emphasis on information transmission, and when recall, reproduction, and summary were treated as dominant indicators of learning.

🤔 Learning Experience Design Challenge

That does not mean foundational knowledge suddenly stops mattering. It does mean we need to distinguish between information that can be easily accessed and the foundational knowledge, judgment, and awareness learners still need in order to think well in a world shaped by human-AI collaboration.

Below, consider doing a quick audit of learning goals for one of your courses, lessons, or other types of learning experiences. The worksheet is provided to help you examine a small set of learning goals, identify where AI is revealing gaps, tensions, and opportunities for revision.

📋 Your Design Decisions

This episode posed five design considerations in the form of questions to help you examine your own learning goals and context. These aren’t checklists or rules. They’re design considerations, and your answers will depend on your learners, your discipline, and your goals.

🤔 Design Considerations

Do your learning goals prioritize content coverage or cognitive capability? Many goals were written when information was harder to access, slower to verify, and more effortful to gather. If a goal is centered on recall, reproduction, or summary, it’s worth asking why. Is that truly foundational knowledge, or is it an inherited assumption from a different information environment?
Does AI use support or undermine the learning goal? AI is not automatically helpful or harmful. For some goals, it may support learning. For others, it may short-circuit the very thinking the goal exists to develop. The key question is where AI strengthens the learning and where it lets students bypass it.
Do your learning goals reflect what authentic, discipline-specific performance looks like when AI is available? The issue is not only whether AI can do the task. It is whether the goal reflects what thoughtful performance in your field actually looks like now that AI is part of the landscape.
Do your learning goals encourage metacognitive awareness? Learners need more than access to AI tools. They need to monitor their own understanding, notice when AI is helping them learn, and recognize when it is helping them bypass the very thinking the learning experience is supposed to build.
What is the best evidence of learning now? If a goal is really about judgment, authentic performance, foundational understanding, or metacognitive awareness, then a polished final product may not be enough evidence. This question helps you notice where your evidence of learning may no longer fit the goal you say you value.

📋 Learning Goal Audit

In this episode, we moved from asking whether an assignment is AI-vulnerable to asking whether the learning goals underneath remain relevant in the age of AI. AI can now improve the quality of what learners produce, but also make it easier for them to offload parts of the thinking process.

While AI use may genuinely support a learning goal, it may undermine it. The point of this Design Brief is to help you consider the difference within the context of your learning experience. To work through these design questions for your own context, download and complete the linked Learning Goal Audit worksheet.

👉 Access the Learning Goal Audit Worksheet
🖥️ Save a copy of this document to complete on your own.
- If working in Google Docs: Go to File → Make a copy
- If working in another program: Go to File → Download and choose your preferred format (e.g., Word or PDF)
🧑‍💻 Select a learning experience you’re worried about and work through each of the following design considerations.
⏰ Plan for about 15-20 minutes to complete the audit.

🎙️ Looking Ahead

In Episode 3 of the AI for Educators Design Lab Podcast, we explore AI literacy and how the design of learning experiences can help students practice it. But before we move on, consider reviewing the optional AI-generated version of a podcast on learning goals in the age of AI at the bottom of this page.

🤖 Optional: S1E2 AI-Generated Podcast Edition

One way to develop your own critical eye for AI-generated content is to compare it with a human-curated analysis of the same topic. The podcast in THIS section is 100% AI-generated and covers the same subject matter as Episode 2 of the Educators Design Lab Podcast on learning goals in the age of AI.

This AI-generated version was created by prompting five AI assistants with a literature review task and then using NotebookLM to synthesize their findings into this audio overview. The voices, images, and words were generated without human fact-checking or editing.

🫣 Behind the Scenes

1. Literature Review

Rather than relying on a single AI assistant, five were prompted, including ChatGPT, Claude, Copilot, Gemini, and Perplexity. They were each given the same task: to dig into the literature focusing on how educators should rethink what students actually need to learn when AI can generate, summarize, and analyze content at scale. Each AI brought different strengths: some excelled at finding current statistics, others at synthesizing academic sources, and some at providing practitioner perspectives.

2. Synthesis

NotebookLM was then prompted to:

Analyze all five reports
Create a range of written summaries and the audio in the “podcast” you heard using AI voices

3. Video & Image Production

The image of the two “podcasters” was created in Gemini. Then, the transcript, slides with captions, and video were generated in Descript, an AI-based media editing app, using audio from NotebookLM.

🤔 Reflect

As you listen, think about what catches your attention and consider:

What feels similar to or different from what was highlighted in the human-created version of this episode?
What would it take to verify the claims and sources in this AI-produced version of the podcast?
Could a tool like this be useful in your own learning or professional practice, and if so, where would you still need human judgment?

Thank you for being here. I look forward to engaging with you in this conversation!

Jennifer Maddrell, PhD
Next Path Design

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