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.