The Syllabus Is Not the Course: AI, Course Design, and the Junk Playground

Generative AI can produce a "complete-looking" university course in minutes: learning outcomes, weekly topics, readings, assignments, rubrics. For anyone who designs courses, that is both an extraordinary capability and a quiet trap. In Episode 107 of Modem Futura, Sean Leahy and Andrew Maynard reflect on more than a year of using AI as a private instructional designer, and on why the most important part of a course may be the part these systems are least able to build.

From sounding board to finished product

Used as a thinking partner, AI can be remarkably helpful. It can test whether a sequence holds together, surface readings no single person would find, and catch overlap between weeks. It is also good at turning existing material into worksheets or self-contained interactive pages that work around the limits of a learning management system. The trouble starts when the tools move from responding to finishing. Ask for help thinking through a course and, increasingly, they return a complete one: plausible, polished, and conventional. These systems are very good at reflecting what has worked before and much weaker at imagining what hasn't been tried (which is no surprise, but increasingly frustrating).

Content is abundant; journeys are designed

The distinction the hosts keep returning to is between content and journey. What remains scarce is the intentional design of experience, the sequence of challenges and moments of realization that carries a learner somewhere new. It echoes John Dewey's argument that education should build on learners' natural impulses toward inquiry, communication, construction, and expression rather than fill them with facts. A course that a single AI prompt could reproduce may be telling its designer something.

A junk playground for the AI age

The episode's most generative idea comes from 1943, when a playground designed by Danish landscape architect Carl Theodor Sørensen opened in Emdrup, Copenhagen. Sørensen had noticed that children were more drawn to piles of scrap than to the playgrounds he designed, so he gave them a space full of loose materials and a play leader to support rather than direct them. He later described it as both the ugliest and the best of his works. Andrew is now exploring what a digital junk playground might look like: a curated space where students build with whatever AI tools they choose and learn through the making.

For educators, that points to a different role, less authority over learning and more what Sean calls an elevated co-participant in it. When nobody yet knows the rules for learning alongside AI, play, experimentation, and care may be among the most rigorous approaches available.

Listen to the full conversation on Modem Futura.

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Show Me the Receipts: the Futures of AI Super-intelligence – Episode 44

Superintelligence “In Sight”? Cutting Through Hype to Keep Humans in the Loop

If you’ve felt whiplash from this summer’s AI headlines, you’re not alone. In our latest episode of Modem Futura, we unpack Big Tech’s bolder‑than‑bold claim that “developing superintelligence is now in sight”—and ask for something simple before we all sprint into the future: receipts. We break down what companies are signaling when they talk about AI systems that “improve themselves,” why that sounds momentous, and where the marketing ends and the evidence begins.

First, definitions that matter. Today’s tools remain narrow—powerful, yes, but specialized. AGI is the hypothetical jump to general capability; superintelligence (ASI) is the further leap beyond any human capability. We explore why those terms are often moved like goalposts, and why declaring ASI “near” without a stable definition confuses the public, policymakers, and practitioners alike, and from my perspective is irresponsible.

Then we zoom into a practical pain point: reliability. When platforms silently change models, tools, or defaults, workflows break (hello GPT-5). In education and professional settings, that unpredictability isn’t just irritating—it’s costly. We share real examples (from transcripts labeled with the wrong speakers to model behavior shifting overnight) and discuss what “enterprise‑grade” should mean for LLMs people depend on.

We also play with the upside—digital twins and imaginative design. If a campus has a high‑fidelity digital twin, why stop at mirroring reality? Why not prototype preferable futures—safer, more inclusive, more sustainable spaces—and test them before we build? Of course, reliability matters there too; when operational systems depend on simulations, unintended tweaks can ripple into the real world.

Across the hour, we push back on technological solutionism—the reflex to cast AI as the single answer to complex, “wicked” problems. Yes, we’re excited about AI’s potential; no, it won’t magically resolve conflict, poverty, or disease without broader social, economic, and political work. Framing ASI as our only lifeboat risks narrowing our imaginations right when we need them most.

Ultimately, we return to our favorite question: What does it mean to be human when machines can emulate so much of what we do? For us, that means staying curious and critical, inviting more diverse perspectives into the conversation, and insisting on transparent claims we can evaluate—before ceding agency to systems we don’t fully understand.

If this episode gave you a useful lens on the AI noise, share it with a colleague, drop a comment with the boldest AI claim you’ve heard and the evidence that would convince you, and subscribe so you don’t miss what’s next.

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If you’d like to dive deeper, jump into the link and listen to the podcast or watch the YouTube video. Join us as we explore the forces shaping our collective future and the urgent need to keep human values at the heart of innovation.

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Subscribe to Modem Futura on a favorite podcast platform, follow on LinkedIn, and join the conversation by sharing thoughts and questions. The medium may still be the massage, but everyone has a chance to shape how it kneads modern culture—and to decide what kind of global village we ultimately build.

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