In this episode, Priten speaks with Chuck Parish, an English teacher at a private Christian school in El Paso, about what it looks like to build an AI elective from scratch inside a community that is still deciding whether to be afraid of the technology or learn it. Chuck's path runs through pastoral ministry, teaching at the bachelor's level in Papua New Guinea, and a year of sixth grade before landing in high school English. The conversation moves between the practical questions he is sorting out for his fall semester and the deeper one he keeps returning to: whether schools are forming the kind of judgment students need to use powerful tools well, or whether they are only writing policies.

Key Takeaways:

• Policy can legislate behavior but it cannot form character. Chuck argues that a clean ban or a strict acceptable-use document is the easy move, and the wrong one. Without a foundation underneath it, students will either ignore the rule or comply for the wrong reasons. The school's one-sentence AI policy treats the question as plagiarism, which misses most of what the technology actually changes.
• A Christian worldview has to address AI the same way it addresses every new tool. The Bible does not name AI any more than it names calculators or television, so the work is in applying an existing foundation to a new technology. Chuck wants students to be able to reason from that foundation themselves rather than relying on him to legislate each case, especially because they will leave the school and lose the legislator.
• Writing instruction was already in trouble before AI arrived. Texting has shifted how students communicate so far that sixth graders submitting "OMG" and "TY" in their papers is no longer surprising. AI does not start the decline in written reasoning; it accelerates a slide that started with the way students already talk to each other. Chuck plans to use handwritten baseline essays to anchor what each student can actually do without help.
• Demonstrating the tool in class is more honest than hiding it. Chuck plans to put ChatGPT on the classroom screen, show how fast it can produce an essay, walk through prompting, and surface the hallucinations and fabricated citations directly. The argument to students is that cutting and pasting cheats them out of the learning, and that integrity has to be taught, not assumed.
• The AI conversation has to include companions and cyberbullying, not just essays. Chuck wants the elective to cover Replika-style companions and image-manipulation tools alongside academic use, because those are the parts students are already encountering outside class. Putting head in the sand, especially in a Christian school context, leaves students to form a worldview about these tools on their own and usually badly.

In this episode, Priten speaks with Tina Austin, an AI educator and professor of biomedical ethics who helps institutions rethink assessment and teaching in the age of generative AI. As ChatGPT disrupted the assumption that polished output reflects student thinking, Tina moved beyond academic integrity concerns to ask a deeper question: what if we redesigned learning around when and how thinking happens, rather than what gets produced at the end?

Key Takeaways:

• Bloom's Taxonomy breaks down because AI collapses the distinction between output and thinking. The old model assumed a polished answer proved learning; AI now makes that assumption untenable, forcing educators to make thinking visible through process rather than relying on products as evidence.
• UnBlooms treats learning as recursive, not hierarchical—and starts with intentional friction. Rather than inverting Bloom's or banning AI, Tina's model requires students to show their initial thinking, engage critically with AI output, and revise with judgment; the shape shifts from a ladder to a spiral where learners don't return to the same place twice.
• Different disciplines protect different kinds of thinking, and AI policy should honor that variation. STEM faculty worry about problem-solving integrity; humanities faculty about voice and nuance; effective AI policy emerges from asking each discipline what thinking they need to safeguard, not from imposing one rule across all fields.
• The most productive AI use in classrooms builds critical skepticism, not efficiency. Having students critique AI-generated lecture summaries or debate where AI diverges from expert knowledge creates genuine engagement; offloading listening itself (via AI note-takers) removes a central learning function and trades visibility into thinking for marginal convenience.
• Higher education's crisis is not new, but AI has made it visible and urgent. Tenure and research incentives protect teaching practices that no longer serve; the opportunity now is to ask honestly whether courses are helping students develop judgment and prepare them for genuine uncertainty—not to add AI on top of unchanged structures.

Tina Austin is an AI educator, researcher, and policy advisor working at the intersection of education, healthcare, science, and emerging technology. Recognized as one of ASU+GSV's Leading Women in AI (2025), featured by OpenAI Academy, and interviewed by CNN, she is one of the most prominent voices guiding institutions toward responsible, human-centered AI adoption. She has led courses at UCLA, USC, CSU, and Caltech spanning critical thinking with AI, biomedical research, regenerative medicine, and ethics.

In this episode, Priten speaks with Laura Schroeder, an 18-year-old student in Germany who spent a year at an American high school and now participates in the Knowledge Society, a global innovation program for ambitious teens. Laura's dual experience across two education systems reveals a critical tension: while schools provide foundation and structure, ambitious students increasingly find their most meaningful learning happening outside formal classrooms, driven by curiosity and real-world project work rather than standardized curricula.

Key Takeaways:

• American schools excel at fostering belonging and passion; German schools prioritize academic depth. The US system's emphasis on extracurriculars, personalized classrooms, and elective variety created a strong sense of community and identity, while Germany's more rigorous curriculum moved students through material years ahead—showing that schools can optimize for different values but rarely achieve both simultaneously.
• Technology in classrooms creates distraction rather than learning gains. Whether Chromebooks or iPads, digital devices enable both research efficiency and constant off-task engagement; Laura's choice to prioritize TKS work over classroom attention reveals that access to devices lets ambitious students opt out, while less motivated students simply drift.
• Project-based learning and standardized structures cannot coexist. Rigid schedules, subject silos, and grades as numbers fundamentally conflict with the flexible, exploration-driven learning Laura values—and attempting to layer PBL onto existing structures, or adding AI without rethinking foundations, misses the deeper architectural problem.
• School provides maturity and awareness that independent learning cannot. Laura credits high school with giving her the lived experience of education's shortcomings, which then motivated her own solutions; skipping formal education earlier wouldn't have accelerated her impact because she lacked the contextual understanding to see the problems that mattered.
• The students most prepared for the future are building it themselves alongside school, not through it. TKS, her project Passion Fruit, and her conference attendance are where Laura develops judgment, iteration, and genuine stakes—school becomes optional context rather than the primary engine of growth for students who have found their direction.

Laura Schroeder is a high school student driven by curiosity and a desire to create meaningful impact. As an Innovator at The Knowledge Society, she builds projects at the intersection of AI, project-based learning, and student agency. Laura is on a mission to reimagine secondary education by returning to first principles and the 'why' behind education - advocating for personalized, interdisciplinary, and foundational education that equips students to thrive in today’s world and the one ahead.

In this episode, Priten speaks with Melvin D. Smith II, a digital learning specialist and computer science teacher at an all-girls school in Maryland where he teaches a required ninth-grade course called Digital Thinking. Smith challenges the assumption that today's youth are automatically tech-savvy and doesn't shy away from restricting access—his school has a no-phone policy—while simultaneously teaching students how to think and communicate with intention in digital spaces. His perspective cuts through both extremes: neither "let them use everything" nor "technology is bad" but rather "understand what you're actually doing and why."

Key Takeaways:

• Being surrounded by technology is not the same as understanding it. Students who've grown up with devices don't automatically know what cookies are, how algorithms predict behavior, or what happens to their data—the access itself teaches nothing without deliberate instruction on how the systems actually work.
• Removing phones from the classroom improved student focus, and students embraced the restriction because it came from them. When administration asked students what they thought about a no-phone policy rather than imposing it, students volunteered the idea and enforced it themselves—suggesting that transparency and student agency can matter more than the rule itself.
• Communication is the foundational skill that makes everything else—including AI use—work. Whether students are writing essays, coding, or prompting AI, the core challenge is knowing how to articulate what they actually want; bad communication produces poor results regardless of the tool.
• AI should be a sparring partner that pushes back, not a butler that does the work. The distinction between using AI to clarify thinking through dialogue and using it to bypass thinking entirely shapes whether it's a learning tool or a shortcut, and teachers need to model and enforce that distinction explicitly.
• The "digital native" myth obscures what students actually need to learn. Today's students need basic digital literacy—not just access to technology—and they need adults to show them responsible use in real time, because peer pressure and the competitive advantage of shortcuts remain powerful forces.

Melvin D. Smith II’s path to tech instruction has been all but a clear one: first planning to be an astronaut to pilot the space shuttle, then changing to become a physician, then neuroscience researcher... 27 years ago he started his career in teaching (formal and informal) science. Adopting the philosophy of STEAM instruction before it became a thing, he fully embraced and utilized the disciplines for the learning environment- in and outside the classroom. Fast forward to his current position at Garrison Forest School in Maryland, Melvin still maintains that practical learning is the most salient and beneficial to developing soft skills and transferable knowledge. Whether in the Digital Thinking class, discussing and practicing the uses of technology to maintain a positive digital footprint; AP Computer Science Principles, where application development coincides with block and text coding; or a brand new course on the history and pedagogical use of AI, his coursework is still rooted in the idea that each student can be reached and succeed if they are given the correct tools, are willing to put forth the effort, and granted a little patience.

In this episode, Priten speaks with Lorin Koch, an educator who has taught across high school, online, and college settings after starting his career in journalism. Koch brings perspective from multiple vantage points—as a classroom teacher navigating AI integration, an online instructor confronting assessment challenges, and a parent of soon-to-be teenagers. Together they explore what happens when students understand the difference between learning and shortcutting but choose the shortcut anyway, and whether responsibility can be taught when the incentive to take a quick way out has never been lower.

Key Takeaways:

• Understanding responsibility is not the same as practicing it. Students conceptually grasp that using AI to do their work for them is wrong, but when faced with pressure to get things done, they often choose the shortcut anyway—suggesting that knowing what you should do doesn't guarantee you'll do it.
• Self-paced, online environments create new accountability problems that have nothing to do with AI. The absence of in-person interaction makes it harder to detect cheating and easier to rationalize it, which means AI hasn't created the problem of student disengagement—it's simply made it more visible and more scalable.
• Your teaching intuition about whether something is AI-generated will become less reliable. As students grow up reading AI-generated text, their own writing will be shaped by those patterns, making it harder for teachers to distinguish between authentic voice and AI assistance based on stylistic markers alone.
• Presenting work through dialogue forces different stakes than submitting text alone. Requiring students to explain their thinking through presentations or discussion boards creates accountability that's harder to fake, even if the source material was AI-generated.
• The gap between high-achieving and struggling students will likely widen because of how students think about time. Students with short-term vision—those thinking about the next 24 hours rather than long-term consequences—are the most vulnerable to AI shortcuts, and they're also the ones who need human attention most.

Lorin Koch is an educator with 21 years experience teaching high school and 3 years as a college instructor of education. He holds an Ed.D. degree from the University of South Carolina. Lorin currently teaches online and in person from Washington state, where he works at Walla Walla University. He also writes and presents on Artificial Intelligence in education, focusing on integrating generative AI into the classroom.

In this episode, Priten speaks with Mary Shawn Newins, a computer science teacher in Greensboro, North Carolina, who arrived in the classroom at sixty with decades of corporate and sales experience but no coding background. Her unusual arc gives her permission to build AI literacy alongside her students rather than ahead of them. What emerges is a classroom culture where curiosity itself—not mastery or fear—becomes the pedagogical goal. She uses practical structures like a "quack" incentive and peer questioning to shift how students see AI: not as a shortcut to avoid, but as a tool that works best when you know what you actually want to learn.

Key Takeaways:

• Curiosity as a pedagogical aim changes everything about how students use AI. When learning for its own sake is the standard—not grades or compliance—AI becomes a catalyst for deeper exploration rather than a means of dodging work. A student asking AI about birds of prey out of genuine interest learns far more than one copying homework.
• Making AI use visible and gamified shifts students from hiding it to owning it. Mary's "quack quack" jar and peer accountability turn using AI into something worth discussing openly. Social transparency works where rules do not.
• Three non-negotiable standards replace prohibition: name the tool, share the prompt, explain the output in your own words. This mirrors citation practices students already know. It's not about policing—it's about maintaining the chain between question, resource, and understanding.
• Strict phones, generous computers reflects a deeper principle about attention and agency. Banning personal devices while enabling desktop computers creates a bounded space for learning. The boundary isn't about rejecting technology; it's about who controls the environment.
• Late-career teachers bring a rare asset: they remember how knowledge worked before AI. Mary's corporate background means she can model learning alongside students without needing to be the expert first. That permission ripples through the classroom.

Mary Shawn M. Newins is a Marketing and Computer Science educator at Southern Guilford High School in Greensboro, North Carolina. She has been a full-time faculty member since Spring 2023 and proudly serves as the school’s AI Champion, supporting innovative and responsible technology integration in the classroom. Mary holds a Bachelor of Science in Education from Bowling Green State University and is an Ambassador for the CodeMonkey High School curriculum, advocating for accessible and engaging computer science education for all students. Before transitioning into education, Mary spent 30 years in the business sector, working across business-to-business sales, retail, direct sales, and operations management. Outside the classroom, Mary is a wardrobe stylist at Chico’s Friendly Center, a denim upcycler, and a creative at heart who enjoys painting.

In this episode, Priten speaks with Alyssa Muhvic, a high school history teacher in Indiana navigating AI's reshaping of her classroom. With experience on her district's AI task force and deep expertise in both AI literacy and equity concerns, Alyssa demonstrates how educators can lead rather than resist technological change. She challenges the assumption that AI's presence signals either inevitable dependence or straightforward disruption, arguing instead that the work is fundamentally pedagogical: helping students develop the judgment to use these tools responsibly while still engaging with core historical thinking skills.

Key Takeaways:

• Treating AI as a search engine reframes citation, sourcing, and critical thinking as one unified practice. Students must learn to evaluate AI outputs with the same skepticism they'd apply to any source—examining bias, verifying claims, and contextualizing information. This makes digital literacy inseparable from historical literacy.
• The equity issue isn't access; it's reliability and responsibility at different price tiers. Paid AI plans produce output 20% more accurate than free versions. When affluent students get more reliable tools, the learning gap widens. Teaching responsible use becomes a justice issue.
• Academic dishonesty with AI reflects overwhelm, not moral failure. High-achieving students risk-taking for perfection; struggling students disengaging entirely. Neither group benefits from prohibition. Both need to understand why checking your work still matters.
• Transparency about your own AI use gives students permission to use it thoughtfully. When teachers hide their tool-use, students either view AI as forbidden or adopt it covertly. Showing your process—and its limits—normalizes critical engagement over sneaking.
• Districts need protected time, not more mandates, to equip teachers as active learners. Asking educators to master AI literacy while managing diploma rewrites, state standards shifts, and dual-credit pipelines is unsustainable. The bottleneck is time, not will.

Alyssa Muhvic is a Social Studies Teacher at Noblesville High School in Indiana, where she has been shaping young minds since 2021. She teaches United States History, Pre-AP World History, and Indiana Studies, and was the driving force behind launching the school's Ethnic Studies course — designing and implementing the curriculum from the ground up. Alyssa earned her degree in General History and Secondary Social Studies Education, with a minor in African American Studies, from Ball State University in 2021. 

In this episode, Priten speaks with Brian Merusi, a special education teacher at Niles High School working with students aged 14–19 who have cognitive impairments. Brian brings two decades of international teaching experience across Abu Dhabi, Poland, Penang, and rural development contexts. The central tension: how do we unlock AI's potential for accessibility and student expression while protecting students from its ethical risks and exploitation?

Key Takeaways:

• Speech-to-text accessibility tools matter more to this population than ChatGPT ever will. For students with typing challenges and diverse communication styles, the ability to speak and have systems capture their thinking credibly is transformative in ways that generative AI is not.
• Pandemic developmental delays hit hardest where social interaction was irreplaceable. Students with cognitive delays experienced compounding losses during remote learning—missing not just content but windows of social and executive development that cannot be fully recovered later.
• Teachers are curators of development, not content deliverers. Brian frames his role as shepherding students toward independent learning and workforce readiness, making technology decisions based on what advances that mission, not on what's trendy.
• AI's dual promise and peril is most acute for students with fewer safeguards against manipulation. The same tools that could help students with dyslexia access reading can also draw them into harmful spaces they wouldn't otherwise encounter—requiring active pedagogical intervention.
• Educators need unified policy guidance, not individual teacher judgment calls on authenticity. Without district-wide clarity on what constitutes authentic work in an AI world, each teacher invents their own standard, creating inconsistency and confusion.

Brian Merusi is a mission-driven educational leader and community developer who combines over four decades of diverse global experience with a passion for practical solutions. Deeply rooted in Special Education and Learning Support across the U.S., Malaysia, the UAE, and Poland, his career also encompasses executive roles as a biotech CEO and development leadership in the D.R. Congo and Uzbekistan. A specialist in technology integration, Brian currently leverages this unique cross-sector expertise to create accessible learning environments where technology opens doors for every student.

In this episode, Priten speaks with Paul Blaschko, an assistant teaching professor of philosophy at Wake Forest University. Paul's work sits at the intersection of liberal education, critical thinking instruction, and course design. The central question driving their conversation: in an era of AI that can generate plausible-sounding arguments and explanations, can we still teach students to think critically—or must we fundamentally reimagine what critical thinking means?

Key Takeaways:

• EdTech should solve existing problems, not create new ones. Paul approaches technology as a tool only when he's already facing a pedagogical challenge. This shifts the question from "what can this tool do?" to "what does my classroom need?"
• YouTube explainers preceded ChatGPT in reshaping how students research and learn. Long before AI, students were outsourcing understanding to video tutorials rather than wrestling with dense texts, revealing a deeper shift in how students approach knowledge.
• Critical thinking instruction requires direct practice with real arguments, not shortcuts around difficulty. There's no substitute for students actually constructing and defending their own positions through dialogue and written work, even when AI can do it faster.
• Scaling critical thinking instruction demands new infrastructure, not just new pedagogy. Paul and his team are testing whether platforms like Think Arguments can help instructors manage the feedback and iteration needed to teach reasoning at scale across institutions.
• AI may not replace the professor's role so much as expand it into explicit curation and judgment. In a world where explanations are abundant, the teacher's value shifts toward deciding which frameworks matter and helping students evaluate competing arguments.

Paul Blaschko is an assistant teaching professor at the University of Notre Dame. He teaches God and the Good Life, a course dedicated to asking the big questions about meaning, morality, and faith. He also serves as the Director of the Sheedy Family Program in Economy, Enterprise, and Society, a program devoted to exploring how the humanities can help us find meaning in work. With Meghan Sullivan, he has co-authored The Good Life Method (Penguin Press, 2022), a book about how philosophy can help us live better lives. He is currently working on a book on the philosophy of work (under contract with Princeton University Press), and is the co-founder of a Notre Dame based tech start-up that aims to solve problems with dialogue on the internet.

In this episode, Priten speaks with Megan Barnes, a PhD student in learning technologies at the University of North Texas and a K-12 librarian with 14 years of experience, about what age-appropriate AI in education actually means. Megan holds dual roles as library director and director of educational technology for early childhood through fourth grade in Dallas, and her research draws on cognitive and affective neuroscience to evaluate how emerging tools interact with child development. The conversation moves through the real-versus-synthetic distinction that young children struggle with, the attention economy driving AI product design, information literacy as a foundation for AI literacy, and why curiosity may be the most important thing educators need to protect.

Key Takeaways:

• Before children can use chatbots, they need a solid concept of real versus not real. Most kindergartners interact with AI through voice and animated characters, adding layers of anthropomorphization that make it nearly impossible for them to distinguish a computer from a person. Megan argues that chatbot-based AI is not developmentally appropriate at this age, and any exposure should be adult-controlled and side-by-side, consistent with American Academy of Pediatrics guidance on co-viewing media.
• The attention economy is becoming a relational economy—and children are the target. The same design logic that removed page numbers from Google search results is now being applied to conversational AI. If a child builds five years of chat history with a platform before adulthood, that relationship becomes a powerful lock-in mechanism. Megan also raises the concern that chat histories are now being used to drive advertising, meaning the tools students use for learning are simultaneously selling to them.
• AI literacy in elementary school means information literacy, not prompt engineering. Rather than teaching young students how to use AI tools directly, Megan focuses on helping them understand who generates information, who validates it, and where AI is already present in their daily lives. During morning announcements, she points out the background remover tool and tells students, "This is AI right here." The goal is building foundational skills for evaluating any new technology, not training on a specific product.
• Every generation of creative technology triggers the same panic—and the pattern holds. Megan draws on her background as a violinist and recording arts student. When Apple's GarageBand launched during her final semester, her synthesizer professor declared it the downfall of music. Instead, it democratized creativity. More people creating doesn't mean everything produced is good, but the tool itself is not the threat. AI follows the same arc.
• Curiosity doesn't need to be taught—it needs to be protected. Young children arrive with natural wonder intact. Megan distinguishes between formal classroom learning and the informal learning space of the library, where autonomy and exploration still drive engagement. The job of early education is not to instill curiosity but to give children frameworks for approaching new things with wonder while still thinking critically, so that instinct survives into adulthood.

Megan E. Barnes is a librarian with over 14 years experience, as well as a Ph.D. student in Learning Technologies at the University of North Texas. Her research focuses on ethical considerations in educational technology adoption and curriculum design. She is currently a research assistant developing curriculum for edge AI and is an ed-tech leader and library director at an independent school. She believes that librarians are information professionals uniquely suited to exploring the intersection of information, technology, and pedagogy.