Margin of Thought with Priten

In this episode, Priten speaks with Brad Bushman, professor of communication at The Ohio State University and a leading researcher on human aggression, about what children learn from violent media and why the same questions now extend to AI and robots. Bushman has spent decades studying how violent television, video games, music, and even scripture shape behavior. The conversation works through the mechanics of how children absorb behavioral scripts from role models, what parents can realistically control, how to weigh the evidence, and what happens as chatbots and companion robots become part of children's lives.

Key Takeaways:

• Children learn behavioral scripts from rewarded role models, including media characters. Bushman explains that kids retrieve "scripts" for how to act in a given situation, and violent characters in media are almost always rewarded and rarely punished. Whether content is active (video games) or passive (TV) matters less than the content itself.
• The most effective parental mediation is the one parents do least. Restricting content and time helps, but watching alongside a child and actively discussing what they see is the most effective approach. Passive co-viewing is the worst option, because silence signals that the violent content is acceptable.
• Content matters more than the medium, but more senses amplify the effect. Reading violent text, hearing violent lyrics, and watching violent music videos all increase aggression, with effects growing as more senses are involved. In one study, scripture passages describing sanctioned killing increased aggression, especially among believers and especially when God was said to approve.
• Media violence is a modest risk factor, but the one we can actually change. Aggression is almost never caused by a single factor. Unlike low IQ, poverty, addiction, or being male, exposure to violent media is controllable, which Bushman frames like a media diet. In lab studies, just 20 minutes with a violent game produces measurable differences.
• Aggression toward robots and AI is a new and open question. Bushman cites HitchBot, a hitchhiking robot destroyed in the US after surviving trips abroad, and notes people are more aggressive toward robots framed as objects than as companions. Whether companion bots that never push back distort young people's expectations of real relationships is, in his words, something theory predicts but the data has not yet tested.

In this episode, Priten speaks with Anand Rao, director of the Center for AI in the Liberal Arts at the University of Mary Washington and professor of communication, about what higher education should preserve and what it needs to rethink as AI reshapes the classroom. Rao has studied AI in digital studies courses for years and co-wrote an early book on ChatGPT in education in March 2023. The conversation moves from the practical work of building AI literacy for students and faculty to harder questions about long-form reading, attention, motivation, and whether a liberal arts education is becoming a luxury just as civic life needs it most.

Key Takeaways:

• The liberal arts should help lead AI development, not just adapt to it. Rao's framing shifted over the past year from "can a residential liberal arts institution survive AI" to a claim that orality, interdisciplinarity, and a pluralistic tradition can shape new AI models and frameworks. The center is deliberately neither pro-AI nor anti-AI; its goal is informed judgment.
• Durable skills are the foundation, but they now have to be deployed in AI settings. The communication, critical thinking, and research skills the liberal arts have taught for millennia still matter, but Rao compares updating the curriculum to teaching Boolean logic and databases in the 1990s. Students need to learn to use AI overviews and deep research tools the way they once learned not to trust the first ten Google hits.
• Education needs friction, and the real obstacle is motivation. Tools like NotebookLM can widen access to difficult texts, but they also remove the productive resistance students work against. A motivated student can do far more with these tools; an unmotivated one can complete the work without learning anything, especially under traditional assessments.
• The threat to attention is selective, not total. Rao pushes back gently on the idea that students have simply lost focus, noting that past classrooms over-represented long-attention students who were selected in. He still sees students enter a flow state for hours on work they care about, which suggests the problem is engagement and relevance more than capacity.
• A liberal arts degree may become a luxury, which raises a civic problem. As cost and return-on-investment pressures push students toward shorter, more specialized credentials, Rao worries about who still gets the general education that supports civil discourse. He argues we have to re-envision K-12 alongside higher ed rather than reform one and leave the other unchanged.

In this episode, Priten speaks with Jane Rosenzweig, director of the Harvard College Writing Center and lecturer in expository writing, about teaching writing in the age of AI. Jane's first-year course, To What Problem Is ChatGPT the Solution?, asks students to study artificial intelligence without outsourcing the work of thinking to it. They discuss why writing is inseparable from thinking, what students lose when they skip the struggle of drafting, and why feedback is a conversation rather than a product.

Key Takeaways:

• Writing is thinking, not output. The point of a writing course is not to produce more papers in the world. It is to give students the experience of working through evidence, weighing ideas, and figuring out what they actually believe.
• Editing skills are not a substitute for drafting. The argument that students can skip the first draft and learn to polish AI output assumes a skill that develops only through drafting. Jane has not seen evidence that students who never write a first draft can revise their way to something meaningful.
• Feedback is relational. A writing tutor often does not know where the paper will end up, and that shared uncertainty is the point. A chatbot can work on what is already on the page, but it cannot build a bridge to the idea a student has not yet had.
• Feedback on demand undermines productive struggle. When students can revise and resubmit to a chatbot at 1 a.m., the friction that makes them reconsider what they think disappears. The decision to skip that friction is being made for reasons other than learning.
• Integrating AI into every course is not a solution. Students can distinguish between AI uses designed to push their thinking and how they will actually reach for the tool under a deadline. Teaching productive uses does not prevent the unproductive ones.
• The deeper challenge is equity, not just pedagogy. A real risk is that students at well-resourced institutions continue to learn how to think while students elsewhere have their instructors replaced with chatbots. Aligning incentives so grades and learning point in the same direction is the work ahead.

In this episode, Priten speaks with Tiffany Brown, litigation counsel at Tech Justice Law, about what accountability looks like when AI products cause real harm. They discuss the wave of product liability lawsuits filed against ChatGPT, why disclaimers and "for entertainment purposes only" language do not insulate companies from responsibility, and how courts are beginning to treat generative AI as a defective product. The conversation also moves into civil rights enforcement, state versus federal action, and the new legal questions raised by autonomous agents.

Key Takeaways:

• Generative AI is being litigated as a defective product. Tech Justice Law has filed cases tying ChatGPT to suicides, suicide attempts driven by AI delusions, and even a school shooting in Canada. The legal theory treats the chatbot itself as a product whose harms were foreseeable and whose deployment was negligent.
• Foreseeability is doing a lot of the work. A book that contributes to a mental health crisis is hard to litigate; a chatbot designed to mimic human emotion and used by a 12-year-old is not. When a company knows or should have known that a product can cause specific harms, the law has tools to respond.
• Disclaimers do not erase liability. A "this may hallucinate" warning, or Copilot's "for entertainment purposes only" terms, do not get a company out from under strict product liability when people are losing their lives. Courts will ask whether the company did enough, not whether it checked a box.
• States are doing the work Congress is not. State attorneys general are opening investigations, state legislatures are passing AI-specific laws, and California recently moved to block the "the agent did it" defense. Federal action is unlikely in the next two to three years.
• The harms cut across demographics. Unlike the social media cases, which centered on minors, AI chatbot cases involve children, older adults, people with disabilities, and even tech-savvy users. The speed and scale of impact is what makes generative AI different.
• Agentic AI raises the stakes again. When a single company can deploy 200 autonomous agents instead of one rogue employee, the scale of potential harm changes the legal calculus. Insurance products are emerging, but Tiffany is skeptical that liability can be outsourced to the agent itself.

In this episode, Priten speaks with Cody Venzke, senior staff attorney with the ACLU's Speech, Privacy, and Technology Project, about who is actually protecting student privacy when the law has not caught up to the technology. They walk through what FERPA and COPPA do and don't cover, the limits of "FERPA compliant" as a marketing claim, how AI surveillance tools are being deployed in schools without adequate vetting, and where parents and teachers can apply pressure when federal law leaves gaps.

Key Takeaways:

• FERPA was written for filing cabinets, not cloud platforms. Passed in 1974, FERPA still grants parents a right to access every record a school maintains about their child, including data held by ed tech vendors. But it has never been enforced by the Department of Education, and individuals cannot sue under it, which leaves most of the work to proactive parents.
• "FERPA compliant" on a vendor website is a marketing slogan. There is no Department of Education certification program. The obligation falls on schools to ensure their vendors actually limit data use to educational purposes, and parents should ask schools how they define "school official" and what contracts allow.
• COPPA stops at the thirteenth birthday. The Children's Online Privacy Protection Act applies only to sites directed at children under 13, leaving teenagers in what Venzke describes as a regulatory wild west. The ACLU argues that data minimization and affirmative consent should be extended to everyone, not gated by age.
• Flat bans on minors using social media will likely lose in court. The Supreme Court has held that minors' First Amendment rights are largely coterminous with adults'. Venzke predicts that age-based bans will be struck down as overbroad, and argues that regulating how platforms collect and use data is a more constitutionally durable approach than restricting speech.
• School AI surveillance is being deployed without testing. Facial recognition, weapons detection, and communication monitoring tools are sold to schools without proof they work as advertised. Venzke cites cases where students have been outed by large language models that misread diary entries as bullying, and argues that high-impact AI uses should require state-level vetting requirements.
• Removing a student's name from a ChatGPT prompt does not make it FERPA safe. Identifying details like "the only Native American student in fifth grade" can still trace back to an individual. Venzke argues teachers should not be left to vet AI tools on their own; districts, states, and procurement processes need to do that work.

In this episode, Priten speaks with Joel Sohn, Deputy Head of School at Head-Royce, a K-12 independent school in Oakland serving roughly 920 students, about how a school can build a coherent approach to AI without retreating into a rulebook. Joel walks through the two-year arc of arriving in fall 2023, identifying early teacher champions, taking them to the Schools of the Future Conference, and using Leon Furze's framework to land a philosophy statement rather than a granular policy. The conversation covers why originality has always been a puzzle, how students have shifted from experimenters to skeptics, and why a simplified nine-word mission is doing more work than any rulebook could.

Key Takeaways:

• Build a philosophy, not a plagiarism policy. Joel draws an analogy to dress codes: the more granular the rule, the more the only thing you see is the violation, not the person. AI use is too varied across math, history, and English classrooms to codify the way schools codified plagiarism a generation ago, and a philosophy gives educators the room to make case-by-case judgments.
• Trust the team first, accelerate later. Joel chose a two-to-three year change trajectory anchored in building educator trust rather than racing to be first. His worry was falling behind by 2027, but the trust groundwork is what made the eventual rollout move quickly and made families comfortable with the rollout.
• Originality has always been a puzzle, and AI just forces the question. Joel pushes back on the assumption that pre-AI student writing was somehow more "original," pointing out that Shakespeare cribbed too and that brain science still cannot pin down what original thought really is. Schools have been asserting certainty they never had, and AI is making that hard to avoid.
• Students are no longer the experimenters they were two years ago. Joel sees the current generation as more anti-AI than in 2023, citing concerns about energy use, corporate ethics, and privacy. Teachers using AI sloppily and shipping obviously machine-generated lessons has accelerated that skepticism, which is why he tells teachers to disclose their AI use and how they checked it.
• Strident anti-AI students need to be interrogated too, not just validated. Joel argues schools should push back when students refuse to engage with AI, not to override their values but to ask whether their stance is rooted in privilege, fear, or genuine principle. The work of school is teaching kids to handle complexity, not to handle any specific tool.

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.