In this trailer of the AI for Educators Design Lab podcast, Jennifer Maddrell, a learning experience designer with a PhD in Instructional Design and Technology, introduces the show's mission to help educators navigate the complexities of AI integration in teaching. Focusing on the balance between the exciting potentials and significant concerns of AI, Jennifer aims to foster reflection rather than offering quick fixes.

The first season will tackle pressing challenges such as academic integrity, AI literacy, and student privacy, offering design considerations and reflective questions to aid educators. Episodes will be released twice a month, along with a free companion Design Brief for deeper engagement, available on our website. Each Design Brief includes a synopsis of the episode's core challenge, key design questions to examine in your own context, and a practical scaffold to help you think through the concepts covered in the podcast.

🔗 Free Design Brief Library: nextpathdesign.com/designbriefs

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00:00 Welcome to the AI for Educators Design Lab Podcast

00:41 The Convergence of Messy and Magical: AI in Education

01:11 Podcast Goals and Structure

01:45 Exploring Pressing Challenges in AI Integration

02:27 Designing Learning Experiences with AI

02:45 Real-World Examples and Design Considerations

03:38 Who Is This Podcast For?

04:04 Upcoming Episodes and Community Engagement

04:58 Join Us on This Journey

As an educator, what do you do when AI can now complete your prior assignments? In this episode, Jennifer Maddrell, PhD, shares how testing and redesigning her own assignment changed her approach to academic integrity in the age of AI. She frames it as a design challenge that educators have professional judgment to tackle.

This episode explores five design questions to help educators explore academic integrity as a learning experience design challenge, not a policing problem:

1. Is your assignment AI-vulnerable?
2. Are you assessing the product or the process of learning?
3. What does this assignment require that AI can't easily replicate?
4. How clear are your expectations for AI use?
5. What does your approach to academic integrity signal to students about classroom culture?

Jennifer also walks through how she redesigned her own literature review without banning AI or using detection software. She concludes with what surprised her along the way: "I started feeling like I was teaching again."

Links mentioned:

🔗 Free Design Brief + AI Assignment Vulnerability Audit: nextpathdesign.com/designbriefs

📬 Next Path Insights Newsletter: nextpathdesign.com/newsletter

🌐 All Next Path Design offerings: nextpathdesign.com/join

00:00 Introduction

00:54 Testing My Own Assignment With AI

01:49 The Academic Integrity Pain Point

02:14 Policing vs. Redesigning 03:28 Why Detection Falls Short

04:55 Framing AI as a Design Problem 06:04 Your Beliefs About AI Matter

07:12 Design Question 1: Is Your Assignment AI-Vulnerable?

08:08 Design Question 2: Product or Process? 09:57 Design Question 3: What AI Can't Replicate

11:01 Design Question 4: Clear Expectations

12:59 Design Question 5: Classroom Culture and Signals

13:59 Redesigning the Literature Review

17:35 Wrap-Up, Resources, and Next Episode

About AI for Educators Design Lab A podcast for educators, instructional designers, and learning leaders exploring how to design meaningful learning experiences when AI changes everything. New episodes are released twice monthly.

If AI can now complete our assignments, does AI change our learning goals? In this episode of the AI for Educators Design Lab podcast, Jennifer Maddrell, PhD, explores how AI not only makes assignments more vulnerable but also prompts a review of traditional learning goals. AI isn't just changing what students can produce. It's also revealing that some of our legacy learning goals were written for a time when recall and reproduction were the dominant aims and indicators of learning.

Using her own literature review assignment as an example, Jennifer considers what students should learn when AI can quickly tackle many learning tasks. She walks through five design questions to help you audit whether your current learning goals are still relevant, sufficient, and aligned with what learners need in a world shaped by human-AI collaboration, and concludes with a preview of Episode 3 on AI literacy coming in April.

1. Do your learning goals prioritize content coverage or cognitive capability?
2. Does AI support or undermine the learning goal?
3. Do your learning goals reflect what authentic, discipline-specific performance looks like when AI is available?
4. Do your learning goals encourage metacognitive awareness?
5. What is the best way to make learning visible?

Check out our other free resources for educators:

• 🎙️Next Path Design Podcast Library: https://nextpathdesign.com/podcast
• 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbrief
• 📬 Next Path Insights Newsletter: https://nextpathdesign.com/newsletter

00:00 Welcome Back

00:25 Why Learning Goals Shift

2:21 Lit Review Wake Up Call

04:56 Five Design Questions

06:03 Coverage vs Capability

07:55 When AI Helps or Hurts

09:46 Authentic Practice Today

11:28 Metacognition with AI

14:14 Evidence Beyond Products

15:51 Wrap Up and Next Steps

17:49 Design Brief and Episode Three

18:54 Final Thoughts and Thanks

Are your students using AI without really understanding it? This episode of the AI for Educators Design Lab podcast, Jennifer Maddrell, PhD, makes the case that AI literacy needs to be a consistent design intention running through the full learning experience. A one-time tutorial, a first-week policy conversation, or an add-on module isn't enough. Students who use AI without understanding how it generates outputs, where it breaks down, or what ethical stakes are involved aren't developing critical judgment. They're outsourcing their thinking.

Jennifer walks through five design considerations for embedding AI literacy intentionally into your learning experience:

1. What does AI literacy and AI fluency mean for your specific learners, in your discipline, at their stage of development?
2. Are you creating opportunities for students to examine AI as a system built by people with human assumptions embedded in it?
3. Where in your existing course do students already evaluate sources, weigh evidence, or question assumptions? Those are your natural integration points for AI literacy.
4. Are your assignments and assessments building evaluative judgment alongside proficiency or primarily rewarding the polish of the result?
5. What structures do you have in place that make AI use visible and reflective? And what are you modeling in your own practice about what a thoughtful relationship with these tools looks like?

The episode closes with a preview of Episode 4, which takes up another concern of educators: As AI takes on a greater presence in a course, what is the role of human interaction and connection?

Check out our other free resources for educators:

• 🎙️ Next Path Design Podcast Library: https://nextpathdesign.com/podcast
• 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbriefs
• 📬 Next Path Insights Newsletter: https://nextpathdesign.com/newsletter

00:00 Welcome and Recap

01:29 The Lit Review Wake-Up Call

02:18 The Literacy Gap

04:12 Five Design Considerations

08:49 Start with the Human Stakes

12:23 Embed AI Literacy in Existing Moments

15:37 Design for Evaluative Judgment

19:05 Make AI Use Visible and Reflective

21:54 Wrap Up and Next Steps

22:13 Design Brief and Episode 4 Preview

As AI is integrated into learning experiences, where is human presence essential? In this episode of the AI for Educators Design Lab podcast, Jennifer Maddrell, PhD, considers the interactions that make learning possible, and makes the case that in an AI-integrated environment, learning is shaped not just by how students interact with technology, but by the people who design, guide, and participate in those interactions.

Drawing on decades of distance and online learning research, including the Community of Inquiry framework and concepts such as transactional distance, she notes that prior edtech research is often ignored in new AI debates. She shares findings from her doctoral research showing social, teaching, and cognitive presence correlate with student satisfaction and perceived learning, but not necessarily with achievement, underscoring that interaction quality matters more than mere connection.

Jennifer explores five (of many!) design considerations related to this topic, including:

1. Where human presence most supports learning,
2. How to allocate tasks between humans and AI,
3. How to design teaching presence when AI handles instructional tasks,
4. Who is most affected when human connection is reduced, and
5. Whether AI introduces a distinct “presence” in learning.

The episode closes with a preview of Episode 5 on data privacy, security, and safety. Check out our other free resources for educators:

• 🎙️ Next Path Design Podcast Library: https://nextpathdesign.com/podcast
• 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbriefs
• 📬 Next Path Insights Newsletter: https://nextpathdesign.com/newsletter

00:00 Welcome and Episode Setup

01:07 AI Hype and Real Use

02:44 We Have Been Here

05:05 Research Lessons on Interaction

08:02 Five Design Questions

08:22 Protect Human Moments

09:57 Backstage vs Frontstage

12:36 Teaching Presence by Design

15:38 Equity and Belonging Risks

17:59 AI as a New Presence

21:26 Wrap Up and Resources

22:55 Next Episode Ethics Preview