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Nuclear power offers huge amounts of round-the-clock energy free of climate-warming pollution. In the United States, it’s also become very expensive to build. As government support grows to bring more nuclear power to the U.S., Prof. Jacopo Buongiorno of MIT joins us to break down how nuclear got so costly and what we can learn from countries with more active nuclear industries.

For a deeper dive and additional resources related to this episode, visit: https://climate.mit.edu/podcasts/e2-nuclear-price-tag

For more episodes of Ask MIT Climate, check out askmitclimate.org. Plus, find us on Instagram, TikTok, and YouTube for outtakes, bonus content, and more climate knowledge from MIT. As always, we love hearing from our listeners; email us at [email protected].

Emil Verner is the Jerome and Dorthy Lemelson Professor of Management and Financial Economics at the MIT Sloan School of Management. His research examines how finance and the broader economy interact, with a focus on the causes and consequences of financial crises — from bank runs and insolvency to debt booms, economic volatility, and political polarization.

Show notes and transcript:

https://news.mit.edu/podcast/podcast-curiosity-unbounded-episode-17-boom-bust-workings-financial-crises

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Teachers are aware that AI is present in schools and learning environments, whether we like it or not. And many feel pressure, internally and externally, to learn and teach some form of "AI literacy". Justin has cautioned that it's too early for us to really understand what AI literacy is, and that just guessing at what might constitute AI literacy might do harm.

Teachers and schooleaders appreciate that warning, but many feel that we can't do nothing. It's essential for teachers to start getting some knowledge about how AI works, to start experimenting with AI powered practices, and think about implementing them into our instruction.

In our research, the most eloquent proponent that teachers should gain, and perhaps teach, some kind of AI literacy is Maureen Russo Rodriguez. Maureen is a Spanish and English teacher at St. Mark’s School in Massachussets. She is a cofounder (with Nate Green) of a network of educators called CoLab, which started in 2024 and now includes over nine hundred educators from over three hundred schools. We talk with her about her path to leading a process by which teachers design their own AI literacy professional development process, and Justin and Maureen try to pin down where they are in agreement, and disagreement.

This episode was produced by Jesse Dukes. You can learn more about Co-lab at https://www.educolab.org/.

We got support for our interview with Maureen from RAISE at MIT: Responsible AI for Social Empowerment and Education. Thanks to Eric Klopfer and Cynthia Brezeal. RAISE also sponsors a series of professional development opportunities around AI for teachers, in a similar spirit to Co-Lab called Day of AI. We had editorial help this week from Steven Jackson, Alexandra Salomon, Adam Brock, Sara Falls, and Steve Oulette.

Teach Lab is a production of the Teaching Systems lab at MIT Justin Reich Director

Salt marshes humming with insects and birds. Mangrove forests with tangled, arching roots. Seagrass meadows that blanket the ocean floor. The world’s coastal saltwater wetlands provide shelter for wildlife, purify water, and protect seaside infrastructure. And as Dr. Julie Simpson of MIT tells us, they also have a climate superpower: drawing down and locking away extraordinary amounts of planet-warming carbon dioxide.

We gratefully acknowledge Professor Heidi Nepf; Ph.D. student Ernie Lee; and undergraduate student Joyce Yambasu of MIT for additional assistance and participation in this episode. Thanks as well to the Waquoit Bay National Estuarine Research Reserve and research coordinator Megan Tyrrell.

For a deeper dive and additional resources related to this episode, visit: https://climate.mit.edu/podcasts/e1-marshes-mangroves-meadows.

For more episodes of Ask MIT Climate, check out askmitclimate.org. Plus, find us on Instagram, TikTok, and YouTube for outtakes, bonus content, and more climate knowledge from MIT. As always, we love hearing from our listeners; email us at [email protected].

Over the last two years, teachers and schools have felt immense pressure to incorporate AI literacy into their curricula. In the fall of 2024, California became the first state to pass a law mandating AI literacy instruction in schools, and several others have since followed suit. In the summer of 2025, the Department of Education released the "AI Action Plan for Education," which stated in part: "The Action Plan encourages schools to teach AI literacy and supports the responsible integration of AI in classrooms. AI is seen as a key education tool to enhance individual student preparation for the real world and to bolster the United States as a leader in AI."

Most major AI companies have pledged significant capital to train teachers or educate students in AI literacy. Google alone has committed over 40 million dollars toward these initiatives, while OpenAI, Microsoft, and NVIDIA have all launched similar donation programs.
But do we actually know what "AI literacy" means? Sam Wineburg doesn't think so. Sam is a professor emeritus of education and history at Stanford and the co-founder of the Digital Inquiry Group. He previously led a landmark study for the Stanford History Education Group (SHEG) that exposed how standard school methods for teaching web literacy were failing K-12 students.

In part one of this two-part miniseries, Wineburg shares his observations on how educators have gotten "literacy" wrong in the past. He suggests there are more responsible ways to adapt to transformative new technologies than to hastily stand up literacy guidelines that may repeat old mistakes.

The eighth season of MIT’s climate change podcast starts next week, and we’ve got some news! TILclimate is now Ask MIT Climate. It’s part of an effort to bring all of our climate change resources under one umbrella and reach learners in as many ways as we can.

We’re also diving into video! Find us on Instagram, TikTok, and YouTube @askmitclimate for outtakes, bonus content, and more climate knowledge from MIT. And we love hearing from our listeners; email us at [email protected].

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Artificial Intelligence is frequently hailed as a transformative force for global supply chains, yet the gap between technological promise and operational reality remains a central challenge for industry leaders. In this episode, host Dr. Matthias Winkenbach, Director of Research at MIT CTL, leads a nuanced discussion on the transition from AI hype to the implementation of functional "decision technology."

Joining the discussion are three researchers from MIT CTL who bring diverse perspectives to the AI landscape. Willem Guter of the MIT Intelligent Logistics Systems Lab unpacks the intersection of machine learning and traditional optimization in warehouse robotics, while Dr. Elenna Dugundji, director of the MIT Deep Knowledge for Supply Chain and Logistics Lab, explains the evolution of demand forecasting and the importance of "deep knowledge" in predictive modeling. Dr. Bryan Reimer, founder of the MIT AgeLab’s Advanced Vehicle Technology Consortium, rounds out the discussion by addressing the critical human factor in autonomous systems. Together, they examine the future of AI in sourcing and procurement, the complexities of human-AI interaction, and the necessity of building decision-support tools that are grounded in real-world application rather than speculative promise.