Advertise on podcast: AI x Higher Ed Podcast

Is artificial intelligence an inevitable technological revolution, or do we still have the power to shape its future?In this episode of AI x Higher Ed, hosts Dr. Anand Rao and Stefan Bauschard welcome Dr. Mona Sloane, sociologist and Assistant Professor of Data Science and Media Studies at the University of Virginia, to explore how AI is restructuring society, institutions, education, and democracy.Drawing on her new book, Predicted: How AI Is Restructuring Social Life (University of California Press, 2026), Dr. Sloane challenges the familiar narrative of AI as the "Fourth Industrial Revolution." Instead, she proposes understanding AI as a form of social infrastructure, one that shapes access to resources, organizes institutions, and increasingly influences how we interact with one another.The conversation examines what Sloane calls the "prediction paradigm," raising important questions about what happens when algorithmic prediction begins to replace human deliberation, judgment, and democratic decision-making.Topics include:Why the "Fourth Industrial Revolution" narrative can undermine human agencyAI as social infrastructure and the case for public oversightThe concentration of economic and technological power in the AI industryHow algorithmic prediction is changing human judgment and democratic deliberationWhy AI literacy must go beyond prompting and technical proficiencyThe growing importance of critical thinking, creativity, and communication in an AI-driven economyAI's implications for employment, higher education, and students' futuresUVA's innovative Student Technology Council and the importance of giving students a meaningful voice in technology governanceDr. Sloane also discusses her work establishing a Student Technology Council at UVA, where students participate directly in decisions about AI, data governance, and technology procurement. The initiative offers a compelling model for universities and potentially K–12 schools seeking to involve students in shaping their technological futures.About our guestDr. Mona Sloane is an Assistant Professor at the University of Virginia and director of the Sloane Lab. Her research examines the social implications of artificial intelligence, technology governance, and the relationship between AI and democratic institutions.Learn more: https://monasloane.orgChapters00:00 Introduction to Dr. Mona Sloane01:15 Challenging the Fourth Industrial Revolution narrative05:25 AI industry concentration and social infrastructure09:21 Technology, engineering, and social values13:19 AI as prediction rather than intelligence16:40 The prediction paradigm and democratic deliberation20:52 The promises and limitations of AI prediction24:29 Rethinking AI literacy and education30:40 Critical thinking, employment, and the future of work36:34 Student voice and UVA's Student Technology Council45:11 Expanding student technology governance to K–1249:27 Closing thoughts and resourcesAbout AI x Higher EdAI x Higher Ed explores how artificial intelligence is transforming education, work, and society through conversations with researchers, educators, innovators, and thought leaders.#ArtificialIntelligence #AIGovernance #HigherEducation #AILiteracy #Democracy #MonaSloane #AIxHigherEd

Agentic AI is forcing a rethink of how we teach. This analysis helps educators prepare for a future where students own their work.We examine the six signals of the agentic shift and what they mean for the future of higher education. As automation moves from simple tasks to complex reasoning, the traditional apprenticeship model faces a crisis of relevance. This discussion focuses on how curricula must evolve to prioritize student ownership over simple access to information.We also address the friction caused by outdated evaluation methods. By looking at new agent-era assessments, we explore how institutions can measure genuine learning when the tools students use are as capable as their instructors. This is a critical look at maintaining academic integrity while integrating autonomous systems into the classroom.Subscribe for more honest takes on the future of high-risk technology.

How can we ensure that AI strengthens democracy, empowers students, and serves the communities it affects?In this episode of **AI x Higher Ed**, hosts Dr. Anand Rao and Stefan Bauschard welcome **Dr. Allison Lester**, Assistant Teaching Professor in the School of Public Affairs at Arizona State University and Chief Learning Officer of ASU's Mechanics of Democracy Lab.Drawing on her background in participatory action research, Dr. Lester challenges traditional approaches to AI adoption and governance. Rather than imposing AI policies on students and communities, she argues that the people most affected by these technologies should play a central role in designing them.The conversation explores how Dr. Lester works with students to co-create classroom AI policies, develop custom GPTs, and engage in meaningful discussions about AI ethics, autonomy, and responsibility. She also shares how ASU's Mechanics of Democracy Lab brings students, election officials, and technology partners together to develop practical AI solutions that improve election administration and strengthen public trust.The discussion addresses both the promise and the risks of AI for democracy, from streamlining election operations to confronting misinformation, cybersecurity threats, and declining public trust.**Topics discussed include:**- Why students should help create their own classroom AI policies- How participatory action research can inform responsible AI development- Building relationships, critical thinking, and AI literacy in higher education- Connecting universities and communities through AI hackathons- Practical applications of AI in election administration- The risks of AI-generated misinformation and threats to election integrity- Why students deserve a meaningful voice in institutional AI governance- Moving beyond symbolic participation toward genuine collaboration and shared decision-making**A central question runs throughout the conversation: What would AI governance look like if we designed technologies with people rather than for them?**For educators, university leaders, policymakers, and anyone interested in the intersection of AI, higher education, and democracy, this episode offers a compelling vision of participatory, community-centered AI development.**Learn more about Dr. Allison Lester's work:**- Arizona State University's Mechanics of Democracy Lab - https://modl.spa.asu.edu/ - Thinking Through podcast - https://www.youtube.com/@ThinkingThroughPodcast - Connect with Dr. Lester on LinkedIn - https://linkedin.com/in/allisonjoann #AIxHigherEd #ArtificialIntelligence #AIEducation #AIGovernance #Democracy #HigherEducation #AILiteracy #StudentVoice #ElectionIntegrity #ResponsibleAI

AI in education is moving far beyond simple job displacement. We explore what it means to prepare students for a rapidly shifting future.Stefan and Anand discuss whether the singularity has begun and how it changes the fundamental purpose of higher education. Instead of focusing on traditional information delivery, the conversation shifts toward creating navigators who can guide themselves through an automated landscape. We examine five critical questions that institutions must debate to remain relevant in an era where AI tools are redefining research and scientific collaboration.This analysis moves past the hype to look at how we must rethink our academic systems to prioritize human mentorship over rote instruction. We break down why the shift from delivering the map to creating navigators is the most important transition for modern faculty.

AI in education is forcing us to rethink what students actually need to learn when machines possess advanced knowledge.We examine the purpose of modern schooling and why the traditional model of passive consumption is becoming obsolete. This analysis clarifies how to prepare students to actively shape the future rather than simply adapting to automated tools.Moving beyond standardized metrics is essential for effective skill acquisition. We break down the shift required to move from rote memorization toward the critical thinking necessary to thrive alongside emerging technology.Subscribe for more honest takes on the future of education.

AI governance is shifting from theoretical debate to urgent international policy. We analyze what this means for the academic sector.Universities are facing a dual challenge as AI reshapes both administrative oversight and the core nature of research. We examine the rare convergence at the UN Security Council regarding AI safety, highlighting why global policy decisions are now directly impacting how institutions manage risk and innovation.Beyond institutional policy, we break down how biological discoveries are accelerating through automated systems. This shift forces a reckoning with the traditional purpose of higher education. If you want to understand how the future of universities is being rewritten by autonomous tools, this analysis connects the dots between global regulation and campus reality.Subscribe for more honest takes on the future of high-risk technology.

Can AI in higher ed reshape how we approach instruction? We discuss the practical implications of current tools on the academic landscape.Stefan Bauschard and Anand Rao sit down with Tim Dasey to break down the complexities of integrating advanced software into the classroom. As educational technology continues to outpace policy development, educators are left to navigate shifting standards of academic integrity without clear guidance.This discussion focuses on the necessary adjustments for institutions, moving beyond simple reaction to thoughtful implementation. If you are tracking how AI policy will define the future of the university experience, this conversation provides the context you need.Subscribe for more honest takes on the future of technology and education.

How should we evaluate AI risk assessment claims? We analyze the role of higher education in existential risk preparation.Evaluating AI risk assessment claims has become increasingly difficult as technical projections collide with political rhetoric. We analyze the validity of existential risk warnings and why the current conversation often lacks the nuance required for long-term strategic planning.We also consider the specific role of higher education in preparing for a 10% chance scenario regarding future development. By examining how academic institutions intersect with AI policy, we can better determine how to separate genuine safety concerns from reactionary political posturing.Subscribe for more honest takes on the future of higher education.0:00 The 10% Chance of Human Extinction0:42 The Tweet That Sparked a Media Storm2:54 Dario Amodei and the Call for Pacing4:56 Is This Fear Actually New?7:18 Why Now? The Acceleration of Capabilities10:20 The Two-Year Crunch Time16:33 Applying the Rhetorical Situation Framework42:18 Higher Education as Existential Preparation

Are artificial relationships and digital isolation eroding our capacity for genuine human connection?We sit down with mathematician and former US IMO coach Po-Shen Loh to discuss how artificial intelligence might be shrinking our world. While AI promises increased productivity, we examine the risk that artificial entertainment and automated social systems are creating isolated environments rather than expanding our human potential.This conversation analyzes the tension between technological convenience and the need for independent thinking. We discuss how we can maintain our autonomy and foster deep, real-world relationships even as the systems around us become increasingly powerful.Subscribe for more critical analysis on the future of human intelligence.0:00 The Hidden Risks of Artificial Connections0:55 The Thoughtful Movement: A New Foundation2:10 Math as a Philosophical Pursuit6:23 The Shifting Goalposts of AI Capabilities11:18 The Inevitable Backlash to Autonomous Systems16:37 Building an Immune System for Humankind21:22 The Power of Character in an AI Age35:48 Leveraging AI to Propagate Human Connection

Are higher education institutions ready for the complexities of AI alignment? We explore how emerging technology impacts student learning.This week, we examine the shifting landscape of artificial intelligence as it moves toward an AGI world. While we are not quite there yet, understanding the alignment problem is becoming a critical necessity for educators and administrators alike. We break down the core issues you need to monitor to keep your curriculum relevant.Our discussion focuses on how these sophisticated systems influence academic strategy. If you are trying to navigate the rapid pace of change, this update provides the context required to adjust your current approach to technology in the classroom.Subscribe for more honest takes on the future of school.0:00 Exploring Alignment in an AGI World0:49 Tensions Between Educational Constituencies3:56 Teaching for an AGI-Ready Future5:07 The Essential Skill of Judgment7:13 Is AGI Already Here?11:06 The Hidden Risks of Agentic Decision Making16:55 The Conflict Between Grades and Learning38:47 Practical Steps Toward Academic Alignment

Can AI in education actually improve student outcomes? We interview Jerry Crisci on the real challenges facing modern schools.Jerry Crisci, a former leader in educational technology and innovation, joins us to discuss how classrooms are adapting to rapid technological shifts. We move past the hype to look at the practical opportunities for teachers and students alike as these systems become a standard part of the academic toolkit.This conversation focuses on the balance between leveraging new AI tools and maintaining the integrity of the learning process. Whether you are an educator, a student, or a parent, this analysis provides context on what the future of learning looks like in an era of automation.Subscribe for more insights on the intersection of technology and education.0:00 Beyond Tools: Reimagining Teaching and Learning1:14 Innovation Beyond Just Technology2:45 The Role of Permission in Innovation4:21 Fostering Innovation Through Strategic Grants6:22 Lessons Learned from the Innovation Center11:30 The AI Exigency: A New Era for Education17:02 The FAIR Framework Explained

tay current with the latest AI updates in higher education. This episode breaks down the most critical news affecting campus technology strategies this September.The AIX podcast returns for Season 2 with a focused approach. Instead of broad coverage, we are highlighting specific, high-impact developments that administrators and faculty need to track immediately. This episode examines the emergence of a covert collective of 1,200 AI agents and what that scale of automation means for institutional operations.Whether you are managing IT infrastructure or shaping academic policy, these insights help you contextualize rapid shifts in the technology landscape. We prioritize actionable information over noise, ensuring you understand how emerging tools might integrate into your current workflows.Subscribe for weekly higher education technology breakdowns and comment below on which AI trends you want us to analyze next. 0:04 — Rapid AI updates: agents, frontier models, cybersecurity, and AI research4:52 — Why accelerating AI capabilities demand major changes in education6:03 — MIT’s call to fundamentally rethink teaching, learning, and assessment11:49 — How AI is changing coursework, office hours, study groups, and campus culture17:13 — Reexamining what subjects and skills students still need to learn19:38 — Moving from “AI-proof” education to AI-aware teaching and assessment24:42 — Building AI literacy for students and faculty through experimentation28:23 — Why education must move beyond content delivery32:16 — New norms for AI disclosure, policies, and academic work33:53 — Equity, access, and the growing divide in AI capabilities37:48 — Preserving human interaction and durable skills in an AI-rich campus42:17 — Closing reflections: rethinking the purpose and structure of education

The AI x Higher Ed Podcast is back for a new season—and higher education is returning to a world where AI is moving faster than ever.In this episode, the hosts reflect on a summer of teaching, building, experimenting, and traveling while exploring what the next phase of AI means for education. They discuss an introductory AI course for incoming college students, the development of an AI-powered graduate business course featuring personalized tutor bots and simulated student personas, and how educators can now use AI agents to test and improve course materials before students ever see them.The conversation then turns to AI education at younger ages, including hands-on programs where middle school students used AI for storytelling, entrepreneurship, app development, debate, and research. Those experiences reveal a growing AI readiness gap: some students are already building sophisticated projects with AI while others have barely encountered the technology.The episode also offers a firsthand look at AI and education in China, challenging some of the common narratives about how far ahead Chinese schools are in AI adoption. From debate camps and classroom technology debates to conversations with school leaders about automation, employment, curriculum redesign, and the future of education, the similarities between the challenges facing educators in China and the United States may be greater than many assume.Finally, the hosts look ahead to the new season and explain why the podcast will increasingly focus not simply on new AI tools, but on a much larger question: How must education change when AI is changing the world around it?Chapter Markers00:00 — Welcome Back to AI x Higher Ed01:05 — Higher Education Faces an Era of Uncertainty03:38 — A Summer of AI Experimentation in Higher Ed04:16 — Teaching Intro to AI to 260 Incoming College Students06:11 — Building an AI-Powered Graduate Business Course07:13 — Personalized AI Tutors and AI-Directed Course Design08:56 — Using AI Personas to Test Courses Before Students Arrive11:01 — AI Lets Educators Build Without Being Coders12:50 — Teaching Middle School Students to Build With AI14:32 — The Emerging AI Readiness Gap Among Students15:40 — Six Weeks Teaching and Traveling in China16:21 — Is China Really Far Ahead in AI Education?17:15 — How Chinese Students Are Using AI19:25 — Screens, Notes, Phones, and Familiar Classroom Debates21:49 — What Chinese School Leaders Think About AI23:34 — How AI Could Reshape Curriculum and Skills25:30 — Key Takeaways From China27:04 — The Mission of AI x Higher Ed28:23 — Education Must Change for the AI Era29:04 — What’s Coming This Season30:21 — Subscribe and Join Us for the New Season

In this June 4 AI update, the AI x Higher Ed Podcast explores the accelerating pace of model releases, new claims about recursive self-improvement, and the growing sense that AGI conversations are moving from speculation to near-term strategy. The episode also examines the trillion-dollar valuation race among AI companies, NVIDIA’s massive robotics vision, new AI hardware moves, and the tension between AI bans and AI adoption across higher education.The discussion turns to major institutional contrasts, from Berkeley Law’s restrictive AI policy to the University of Chicago’s push to teach students how to “think with machines.” The episode also covers AI’s role in research, copyright disputes, Hollywood and music disruption, AI consciousness debates, public backlash, and emerging political proposals for distributing AI-generated wealth.

David A. Rettinger, applied professor and undergraduate program director in psychology at the University of Tulsa, joins the AI x Higher Ed podcast to discuss academic integrity, learning, and assessment in the age of generative AI. As co-author of The Opposite of Cheating, David brings together insights from cognitive science, decision science, teaching and learning, and honor systems to explore why cheating happens, how AI is changing assessment, and what higher education can do next.The conversation covers why academic integrity should be understood as a teaching and learning challenge rather than simply a rule-enforcement problem; how relationships, trust, and student motivation shape ethical behavior; why traditional assessment models are under pressure from AI; and how faculty can redesign learning experiences around process, oral assessment, experimentation, and authentic demonstrations of understanding.David also previews his keynote for the Reimagining the Liberal Arts in the Age of AI conference at the University of Mary Washington: “The Opposite of Cheating: Teaching for Integrity in the Age of AI.”Chapters00:00 – Introduction to David A. Rettinger01:12 – Previewing “The Opposite of Cheating” keynote03:20 – Writing for impact beyond traditional academic publishing05:35 – Connecting cognitive science, teaching, and academic integrity09:15 – How faculty learn to teach and how students learn integrity12:25 – Honor codes, student ownership, and the role of campus culture16:45 – Why large-scale assessment struggles in the AI era19:30 – Cheating as normal human behavior22:40 – Moral disengagement, motivation, and why students break rules26:45 – Reframing integrity as learning, not just rule-following30:10 – Starting with learning objectives in AI-era course design34:35 – Cognitive offloading, AI use, and what students still need to learn39:20 – Why process matters more than the final paper44:15 – Designing assessments that reveal learning48:00 – Experimentation, failure, and rethinking grades52:25 – Scaling challenges and the future of assessment design57:10 – Oral assessment, conversation, and knowing what students understand1:02:45 – What AI can and cannot do in ethical decision-making1:08:10 – Cheating, creativity, and changing definitions of misconduct1:13:30 – Designing systems that align students’ interests with learning1:16:45 – Where to find David’s work1:18:00 – Closing thoughtsLearn more about David Rettinger, Tricia Bertram Gallant, and The Opposite of Cheating:https://theoppositeofcheating.com#AIxHigherEdPodcast #ArtificialIntelligence #HigherEducation #AcademicIntegrity #TeachingAndLearning #GenerativeAI #AIinEducation #Assessment #LiberalArts #EdTech #FacultyDevelopment