S1E5: Designing for Data Privacy and Security in AI-Integrated Learning

S1E5: Designing for Data Privacy and Security in AI-Integrated Learning

Who's responsible for data privacy and security in AI-integrated learning? In Episode 5 of the AI for Educators Design Lab podcast, Jennifer Maddrell, PhD, argues that these issues aren't just IT and compliance concerns, or problems tech vendors should monitor and control. They're core design responsibilities for educators, learning experience designers, and educational leaders that shape everyday tool choices, workflows, and prompt decisions. This episode was recorded amid reports of a major Canvas/Instructure security incident that may have exposed data for up to 275 million students, faculty, and staff. While large-scale breaches grab headlines, Jennifer argues the more common everyday risk is far quieter. It could be a well-intentioned teacher pasting student names, grades, or full assignments into tools like ChatGPT, Claude, or Gemini without pausing to consider where that data goes, how long it's retained, or whether it's used to train the model. To work through this challenge, Jennifer walks through five design considerations along an arc that begins with the educator and works outward to students, families, and community: 1. Educator grounding: Building a privacy-aware workflow with habits like multi-factor authentication, pseudonyms in prompts, no-training modes, and treating new AI features as new tools 2. AI tool selection: Recognizing that data protections aren't binary but exist on a spectrum from free consumer accounts to paid personal plans to enterprise and education-specific licenses with Data Processing Agreements 3. Data minimization during use: Asking what the least amount of personal data a task actually requires, and paying attention to which learners would bear the greatest harm if something went wrong 4. Teaching privacy literacy: Building privacy as a skill students actively practice, not just a rule they follow 5. Transparency and consent: Knowing the legal and ethical obligations to inform students and families, especially for minors, and adding clear syllabus language, real opt-out alternatives, and parent-facing disclosures The episode closes with a preview of Episode 6, which extends the equity conversation into the access dimension to ask: what happens when AI integration assumes devices, connectivity, or paid tools that not all students have? * 🎙️ Next Path Design Podcast Library: nextpathdesign.com/podcast [https://nextpathdesign.com/podcast]  * 🔗 Design Brief Library (companion worksheets for every episode): nextpathdesign.com/designbriefs [https://nextpathdesign.com/designbriefs] * 📬 Next Path Insights Newsletter: nextpathdesign.com/newsletter [https://nextpathdesign.com/newsletter] 00:00  Welcome and Episode Focus 01:15  The Canvas/Instructure Breach — A Wake-Up Call 02:46  Everyday Classroom Privacy Risks 03:46  Invisible Data Collection in AI Tools 05:28  Why Privacy Is Every Educator's Job 06:11  Five Design Considerations Overview 07:43  DC1: Educator Grounding and Privacy-Aware Habits 11:08  DC2: Choosing Safer AI Tools 14:32  DC3: Data Minimization and Vulnerable Learners 17:29  DC4: Teaching Student Privacy Literacy 19:43  DC5: Transparency, Consent, and Family Communication 23:18  Wrap-Up and Preview of Episode 6 Other Mentioned Sources: * The Future of Privacy Forum [https://fpf.org/] * 2026 Canvas security incident - Wikipedia [https://en.wikipedia.org/wiki/2026_Canvas_security_incident] * Protecting Student Privacy While Using Online Educational Services: Requirements and Best Practices [https://studentprivacy.ed.gov/resources/protecting-student-privacy-while-using-online-educational-services-requirements-and-best]  * Student and Educator Data Privacy | NEA [https://www.nea.org/professional-excellence/student-engagement/tools-tips/student-and-educator-data-privacy]  * Report - Off Task: EdTech Threats to Student Privacy and Equity in the Age of AI - Center for Democracy and Technology [https://cdt.org/insights/report-off-task-edtech-threats-to-student-privacy-and-equity-in-the-age-of-ai/]  * Problems with Privacy and Misuse of Student Data | Fairplay for Kids [https://fairplayforkids.org/wp-content/uploads/2020/01/Privacy-and-Misuse-of-Student-Data.pdf]  * CDT – Hand in Hand: Schools’ Embrace of AI Connected to Increased Risks to Students [https://cdt.org/wp-content/uploads/2025/10/FINAL-CDT-2025-Hand-in-Hand-Polling-100225-accessible.pdf]  * Critical AI Data Governance Gap in Higher Education: What Institutions Must Do Now [https://www.kiteworks.com/cybersecurity-risk-management/higher-education-ai-governance-gap-data-security-compliance/]  * The Impact of AI on Work in Higher Education | EDUCAUSE [https://www.educause.edu/research/2026/the-impact-of-ai-on-work-in-higher-education] * Data Security and Compliance Risk: 2026 Forecast Report [https://www.kiteworks.com/sites/default/files/resources/kiteworks-report-education-ai-governance-data-security-compliance-2026-report.pdf]

S1E4: Designing for Human Presence in AI-Integrated Learning

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 [https://nextpathdesign.com/podcast] * 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbriefs [https://nextpathdesign.com/designbriefs] * 📬 Next Path Insights Newsletter: https://nextpathdesign.com/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

S1E3: AI Literacy: Helping Learners Think Critically About AI

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 [https://nextpathdesign.com/podcast] * 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbriefs [https://nextpathdesign.com/designbriefs] * 📬 Next Path Insights Newsletter: https://nextpathdesign.com/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

S1E2: Rethinking Learning Goals in the AI Era

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 [https://nextpathdesign.com/podcast] * 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbrief [https://nextpathdesign.com/designbreif] * 📬 Next Path Insights Newsletter: https://nextpathdesign.com/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

S1E1: Academic Integrity in the Age of AI

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 [https://nextpathdesign.com/designbriefs] 📬 Next Path Insights Newsletter: nextpathdesign.com/newsletter [https://nextpathdesign.com/newsletter] 🌐 All Next Path Design offerings: nextpathdesign.com/join [https://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.