Azadeh and Guy explore how AI can support qualitative research in education, examining a 2025 study on using ChatGPT for thematic analysis. They discuss the six-step Braun and Clark methodology, the importance of structured prompting, and challenges like maintaining analytical depth. Drawing from their own research experience, they share practical insights on AI-assisted qualitative coding, including tips for defining units of analysis and preventing surface-level results. Perfect for educational researchers curious about integrating AI tools into qualitative analysis workflows while maintaining methodological rigor.

Azadeh and Guy explore groundbreaking 2025 research from Singapore on teaching students to use AI critically rather than blindly. The study reveals how brief interventions can dramatically improve creativity and reduce reliance on thoughtless AI. Then Guy shares a controversial prediction: could AI agents soon transform academic research, leaving fewer researchers to do more work? The hosts debate whether education and research will face the same disruption as coding. Essential listening for educators navigating AI integration, critical thinking instruction, and the future of knowledge work.

Azadeh and Guy tackle a provocative premise: access to AI literacy is becoming a civil rights issue. They argue that when well-resourced parents push back against AI in schools, they may inadvertently harm the students who need exposure most: those without devices or guidance at home. The conversation goes global, drawing on research from Ghana and Burkina Faso to highlight how lack of infrastructure (electricity, internet, even roofs) creates compounding disadvantages. But there are practical paths forward: solar-powered computer labs, lightweight open-weight models running offline, and local Wi-Fi networks that don't require broadband. The key bottleneck? Teacher preparation. Teachers need to experience AI's value for themselves before they can teach it. The episode closes with a lighter segment on personal AI use, including a Daniel Pink–inspired "self audit" via AI and a last-minute ground beef recipe save.

Guy shares takeaways from AACTE 2026 in New Orleans, where he participated in a learning lab focused on data sovereignty, attended a talk by Punya Mishra on AI, and listened to a panel discussion about digital technology in teacher education. The overarching theme from the conference was that teacher educators must prepare future teachers for an AI-integrated world, regardless of personal opinions about the technology. A notable sidebar discussion centered on AI-powered simulations as a scalable, cost-effective way to expose preservice teachers to rare but important classroom scenarios, like student violence or working with students who have uncommon disabilities.

In Episode 5 of Azadeh and Guy on AI, Guy and Azadeh dig into one of the most pressing, and underexplored, questions of the AI era: who actually owns our data? Guy draws on two compelling examples that reframed his thinking: Finnish researchers who gave elementary school students real control over their digital traces, and indigenous First Nations communities wrestling with whether to share their cultural knowledge with AI platforms at all. Both stories surface the same core tension, between the power that comes from participating in the AI ecosystem and the loss of control that participation demands.

The conversation takes a sharper turn when they tackle the role of regulation. Guy admits feeling genuinely conflicted: yes, we need laws that protect people’s data sovereignty, but the recent examples of what governments actually do with AI authority, from authoritarian censorship to pressuring companies like Anthropic over military use, are sobering. His conclusion: regulation that enfranchises people, yes. Regulation that restricts democratic freedoms, absolutely not. It’s a hard line to walk, but as both hosts agree, we have to do something.

In this episode, Azadeh and Guy welcome Dr. Jordan Wheeler, a colleague at UNL who specializes in psychometrics and educational measurement. The conversation explores how generative AI can be integrated into the survey and scale development process—specifically for measuring how engineering students perceive and use AI, while emphasizing that human expertise remains essential at every step.

In this episode, we unpack junkification in academic research, and why AI may dramatically accelerate it.

Drawing on a recent 2025 paper by two Australian scholars, Carl Rhodes and Martina Linnenluecke, we examine how publish-or-perish incentives, platform capitalism, and pay-to-publish models are reshaping academic knowledge production. The core claim is not that AI caused the problem, but that AI lowers the marginal cost of producing “good enough” research to near zero, creating conditions where quantity overwhelms quality.

We explore how the explosion of journals, the strain on peer review, and metric-driven evaluation systems disproportionately harm early-career researchers, scholars in the Global South, and those outside elite institutions. We also confront uncomfortable realities: fabricated data, citation gaming, collapsing review capacity, and the limits of trust-based systems in an era of automated text generation.

The conversation ends with a critical question for the future of academia: if every single metric can be gamed, what kinds of evaluation systems and institutional courage are required to protect research integrity in the age of AI?

Link to article https://journals.sagepub.com/doi/10.1177/13505084251399576

Azadeh and Guy are joined by Dr. Inoussa Malgoubri to discuss how AI has shaped his academic life and research practice. The conversation explores AI as a powerful tool for educators and researchers, addressing concerns about cognitive debt, bias, and maintaining human agency.

Dr. Malgoubri shares concrete examples of using AI ethically in his dissertation, including transcription, qualitative coding, and verification, and offers practical advice for graduate students on literature review, brainstorming, and polishing academic writing.

A thoughtful look at keeping humans in the loop while using AI responsibly in research and education.

Season two is officially here! 🎉 Azadeh and Guy are back to kick off the new year, a new semester, and a fresh chapter of Azadeh and Guy on AI. In this season premiere, we reflect briefly on where we’ve been-AI in education, research, language learning, and achievement emotions-and then turn our focus forward.

What’s coming next? Responsible and ethical AI in teaching, real-world research pitfalls, interviews with educators and researchers, and honest conversations about the promises and problems of AI’s rapid expansion. From co-teaching with AI and automating research workflows to questioning big tech, wrapper tools, political backlash, and the growing concern over “AI junk research,” this episode sets the tone for a thoughtful, curious, and critical season ahead.

If you care about AI beyond the hype-how it’s actually used, misused, resisted, and reshaped in education and research-this season is for you. Buckle up. It’s going to be a fun (and careful) ride!

In the Season 1 finale of Azadeh and Guy on AI, we focus on a side of AI that’s often overlooked: emotion. Drawing on two recent studies in AI-supported language learning, we explore how AI influences students’ anxiety, confidence, enjoyment, and willingness to communicate. Across university and teenage language learners, the research shows that AI doesn’t replace teachers or human connection. It works best when it augments them. When paired with strong teacher support and psychological safety, AI can reduce anxiety, build confidence, and help learners find their voice in a new language. We reflect on what this means for educators and learners as we close out Season 1 and look ahead to what’s next.Season 2 is coming soon! Until then, keep listening and keep the conversation going.Links to the papers:https://onlinelibrary-wiley-com.libproxy.unl.edu/doi/10.1111/ejed.70157https://bera-journals-onlinelibrary-wiley-com.libproxy.unl.edu/doi/full/10.1002/berj.4191