What Happens When School Is Not Enough? - Laura Schroeder

Should We Rethink the Liberal Arts in the Age of AI? - Anand Rao

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.

If AI Writes, Who Thinks? - Jane Rosenzweig

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.

Can the Law Hold AI Accountable? - Tiffany Brown

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.

Who Is Protecting Student Privacy Right Now? - Cody Venzke

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.

How Might Schools Make Sustainable AI Policies? - Joel Sohn

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.