Design Brief 1: Academic Integrity in the AI Era -

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, such as academic honesty, student privacy, and inclusion.

🎙️ Podcast Focus

In Season 1, Episode 1 of the AI for Educators Design Lab podcast, we dig into the question that’s keeping so many educators up at night: What do we do when AI can complete our assignments?

As we kick things off, I want to briefly tell you what this podcast is about and why I started it. A couple of years ago, I tested one of my own assignments in ChatGPT, Claude, Gemini, Copilot, and Perplexity. It was a literature review I’d assigned for years, and what came back in seconds looked a lot like what I’d been asking students to produce over several weeks. That was a hard realization, and it’s where this episode begins.

🤔 Learning Experience Design Challenge

Rather than treating AI as a cheating problem to police, the episode reframes academic integrity in the age of AI as a learning experience design challenge. The core argument is that AI-vulnerable assignments reveal what those assignments are actually measuring, and considers academic integrity through design as an alternative to detection. Importantly, my focus is on the agency we have as educators, instructional designers, and learning leaders to design effective learning experiences and make informed decisions in the face of this new reality.

📋 Worksheet: AI Vulnerability Audit

This episode posed five design considerations in the form of questions to help you examine your own assignments 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

Does your assignment measure human learning or AI capability? If AI can produce a passing submission, what does that reveal about what the assignment actually measures?
Are you assessing the product or the process of learning? Do students have opportunities to show how their ideas evolved, what decisions they made, and why?
What skills does this assignment require that AI cannot easily replicate? What parts require judgment, context, or reasoning that AI currently struggles with?
How clear are your expectations for AI use? Do students truly understand what’s acceptable, or are they making assumptions that might not match yours?
What do your expectations about AI use signal to students? Does your approach build trust and invite informed decision-making, or does it create adversarial dynamics?

🤖 AI Vulnerability Assignment Audit

To work through these design questions for your own context, download and complete the linked AI Vulnerability Assignment Audit worksheet. This audit walks you through these design considerations for one of your own assignments. The goal is to help you identify where it might be AI-vulnerable and what design changes could make student thinking more visible.

👉 Access AI Vulnerability Assignment 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)
🧑‍💻 Choose one assignment you’re concerned is AI vulnerable and work through each section of the worksheet.
⏰ Plan for about 30–45 minutes to complete the audit.

There are no right answers. Use this as an opportunity to clarify the trade-offs so you can make more informed design decisions about assignments in your context and for your learners.

🎙️ Looking Ahead

Coming up in our next episode, we explore how AI is challenging us to rethink what essential learning goals look like when AI can handle so much of what we used to ask students to do. But before we move on, consider reviewing the optional AI-generated version of a podcast on academic integrity in the age of AI at the bottom of this page.

🤖 Optional: AI-Generated Podcast on This Topic

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 1 of the Educators Design Lab Podcast.

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 on academic integrity and the design of learning experiences in the age of AI. 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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