Tag Archives: Teaching with AI

Polygnosis and the New Frontier: Finding Our Bearings

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Continuing reflections from the Connecticut College AI & Liberal Arts Symposium

If the first post was about waking up, this one is about finding your bearings.

At the symposium, the mood shifted. The panic over AI—the moral fog, the productivity hype—gave way to something quieter and braver: curiosity. Once we accepted that AI is no longer a visitor but a roommate, the real questions emerged:

How do we live and learn beside it?
How do we create with a system that accelerates answers but doesn’t guarantee understanding?

Those questions led me back to a word that keeps earning its keep the more I use it: polygnosis—many ways of knowing.

Where the Term Came From (and Why That Matters)

Confession: I thought I coined polygnosis. It arrived during a late-night exchange with a generative model while I was trying to name the frontier beyond “inter-” and “trans-.” A neat ego moment—until I realized the idea has been with us for millennia in different clothing. The point isn’t originality; it’s precision. Polygnosis names what I couldn’t quite put my finger on: not a new discipline, but a way of composing knowledge across differences—human and non-human—without flattening them.

Polygnosis isn’t a theory; it’s a temperament for learning beside machines.

 

From Disciplines to Directions

Interdisciplinary work still starts with the disciplines—a chemist and a poet at the same table, each speaking their dialect.

Transdisciplinary work stretches the table, turning it from a fixed surface into a living workspace—one where ideas, methods, and even machines can join the dialogue.

Polygnosis begins there. It sets a stage where the conditions of learning can play out—less about blending fields, more about cultivating the stance that lets multiple knowledges coexist and collaborate. In a world where learning happens with other intelligences, that stance isn’t a luxury; it’s survival literacy.

And yes, that’s a liberal-arts value at its core: the capacity to survive—and thrive—through changing times by reading context, holding paradox, and practicing judgment.

The Mirror Problem (and Kranzberg’s Reminder)

A recent Harvard study asks a piercing question: Which humans are our models built on? The answer won’t shock you—mostly Western, English-speaking, educated, highly online adults. That narrow slice of humanity quietly became the template for what many models assume is “normal.”

We can’t call that neutrality. We can only call it inheritance.

Melvin Kranzberg’s First Law lands squarely here: technology is neither good nor bad; nor is it neutral. Models aren’t villains or saints; they’re mirrors tuned to the cultures that polished them. The risk isn’t that they’re “biased”—it’s pretending they’re not.

So the task isn’t to scrub away particularity; it’s to expand the story the machine can tell and we can interpret. That is polygnosis in practice.

From Bias to Balance

Every prompt is a vote for what counts as knowledge. If we want better answers, we need better questions:

Whose perspective might be missing here?

How would this read through another cultural lens?

What assumptions am I reinforcing by treating this response as universal?

This isn’t performance or politeness. It’s epistemic honesty—being clear about where knowledge stands when it speaks. We’re not optimizing for optics; we’re optimizing for accuracy with context.

The Liberal Arts as a Living Laboratory

The liberal arts have always practiced polygnosis—even if they never used the word. They train the muscles we need now: interpretation, comparison, translation, discernment.

Keep it simple and grounded:

In a composition class, a student uses an LLM to draft an intro. The work isn’t to grade the draft; it’s to ask how the model thinks and where the student’s voice should diverge.

In biology, image models help visualize ecosystems. The lesson isn’t “cool pictures”; it’s What does the model miss about living systems, and why?

In history, we compare a human summary and an AI summary of the same source, side-by-side, then mark what each foregrounds and erases.

Down-to-earth, doable, honest. Not spectacle—pedagogy.

Polygnosis as Ethos (Not Coursework)

To theorize polygnosis is to pin the butterfly. I’d rather see it fly.

Polygnosis is an ethos—a discipline of attention. It’s curiosity with a spine, method with humility, design as dialogue. In classrooms, studios, and labs, it looks like:

  • Co-creating a syllabus with an AI, then annotating its blind spots in the margins.
  • Asking a model to produce three interpretations from different cultural frames—and having students choose, remix, or reject them with reasons.
  • Building default prompts that say: “Answer without assuming age, ethnicity, or region; if an assumption is necessary, make it explicit.” Not for show. For truth.

What We Should Do Next (Practical and Small)

Set defaults that widen the lens. “Unless specified, respond for a general audience; flag any cultural or demographic assumption you made.”

Teach self-audit. Before accepting an answer, ask the model (and the student): What perspective did this come from? What would challenge it?

Diversify inputs. If we feed narrow corpora, we’ll get narrow mirrors. Bring in sources—texts, datasets, voices—that a general model is likely to underrepresent.

Reward interpretation. Grade the reasoning about outputs, not just the outputs themselves. We’re cultivating readers of intelligence, not just users.

None of this requires a new program or a moonshot grant. It requires habits, modeled consistently.

Polygnosis in Practice: Building Tools That Build Us

Theory becomes real when we put it to work. I’m thrilled to be teaching a class in the spring, Crafting Digital Identity, which aims help learners create professional web presence. This is where polygnosis isn’t an abstraction—it’s practice.

Students learn to prompt and code custom tools that help them craft portfolio narratives, articulate their professional voice, and position themselves strategically for graduate programs or the workforce. They build the tools, then use those tools to build their presence—websites, social media strategies, personal statements that sound like them, not like a template.

But here’s where polygnosis shifts from concept to practice: we don’t accept outputs at face value. We interrogate them using frameworks like Dakan and Feller’s 4Ds (Discover, Discern, Design, Deploy) and Mike Caulfield’s SIFT (Stop, Investigate the source, Find better coverage, Trace claims back to the original context). These aren’t just media literacy buzzwords—they’re diagnostic lenses. They help students ask: Where did this voice come from? What cultural assumptions are baked into this recommendation? How do I preserve my authenticity while leveraging algorithmic assistance?

This is polygnosis applied: using AI to amplify human agency while maintaining critical distance. Students aren’t consumers of AI-generated content; they’re collaborators who understand the system well enough to bend it toward their actual needs. They learn to see the seams, question the defaults, and design with intention.

The result? Graduates who can code a custom GPT to help draft a cover letter, then edit it with the discernment of a liberal arts thinker. Students who understand that personal branding isn’t about projecting an image—it’s about translating their complex, multifaceted selves into narratives that resonate across contexts. Professionals who can speak fluently to both recruiters and academics because they’ve learned to toggle between knowledge systems without losing coherence.

That’s the frontier we’re walking: not just teaching about AI, but teaching with and through AI in ways that make students more capable, more critical, and more themselves.

Finding Our Bearings

We’ve moved past the question of whether AI belongs in the academy. It’s already in the room, auditing everything. The better question is how we keep our humanity expansive, not defensive.

Polygnosis gives us a compass, not a map. It doesn’t dissolve disciplines; it stretches the table so more kinds of knowing can join the work. It asks us to prefer coherence over consensus, dialogue over default, and context over speed.

We don’t need a new field. We need a new fidelity—to curiosity, to complexity, to the courage of unknowing.

The frontier isn’t out there anymore. It’s between us. Within us. And, increasingly, beside us.

Next time: How the 4Ds and SIFT frameworks anchor practical AI pedagogy—and why critical making matters more than critical thinking alone.

Co-Creating with the Machine: What AI Reflects Back

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A reflection on the 2025 Connecticut College AI & Liberal Arts Symposium, exploring how AI is reshaping liberal arts education through cross-disciplinary learning, human connection, and a shared sense of awakening.

Introduction

Across three autumn days at the AI and the Liberal Arts Symposium at Connecticut College, the conversations felt less like a conference and more like a collective act of awakening.

First, a huge thank-you to our colleagues and friends at Connecticut College for hosting such a fantastic event. This was, without question, the best professional development experience I’ve had since the pandemic.

I only found out about it a few weeks ago, so I wasn’t able to submit a proposal this time—but I highly recommend the symposium to anyone interested in the intersections of AI and the liberal arts. It’ll return next year, and you can bet I’ll be responding to the call for proposals to share the exciting work we’re doing at Skidmore that contributes to this dynamic and evolving space.

I arrived early and after registering, participated on a group tour of Connecticut College’s amazing arboretum. It’s open to the public and I highly recommend a visit.

College Center at Crozier-Williams

Welcome packet

Vista with weeping conifers

Reflection in the Pond

 

AI isn’t just a new tool for the liberal arts — it’s a mirror.

Everywhere, that mirror reflected something back: our assumptions about knowledge, our fatigue with disciplinary boundaries, our uneasy faith in human judgment. Some framed AI as a pedagogical partner, others as a provocation. But beneath every debate ran a shared undercurrent — that the liberal arts must not retreat from AI, but reinterpret themselves through it.

Beyond Silos: Following the Phenomenon

One recurring theme was the generative convergence of disciplines, where boundaries became bridges. Panelists from across fields described how AI resists neat categorization: it writes like a humanist, reasons like a scientist, and fails like an artist.

A digital humanities panel explored how generative tools can help students see structure in story or bias in data. An environmental studies group used AI-generated imagery to visualize climate change as cultural narrative rather than scientific abstraction. A philosophy instructor co-taught a course with a data scientist, letting students interrogate both logic and ethics in the same breath.

These moments revealed a shift — not from one discipline to another, but beyond discipline entirely — into what several speakers called transdisciplinary learning: inquiry that follows the phenomenon, not the field.

It’s an approach that feels truer to the liberal arts than ever — dynamic, synthetic, and driven by wonder rather than walls.

The Liberal Arts Awakening

Across sessions, a pattern emerged — one that keynote speaker Lance Eaton gave a name to in his address, The Sleep of the Liberal Arts Produces AI. His metaphor caught fire throughout the symposium. In panels and workshops afterward, people kept returning to it: the idea that AI didn’t replace us — it revealed where we’d already fallen asleep.

“AI didn’t replace us — it revealed where we’d already fallen asleep.”

That sleep took many forms.

Dismissal — the academy’s habit of treating new media and emerging technologies as distractions rather than dialogues.
Fetishization — the way we mistake performance of intellect for presence of curiosity.
Externalization — the quiet outsourcing of our public mission to private systems and paywalled knowledge.

Panelists didn’t treat these as abstract critiques; they tied them to practice. A librarian showed how paywalled scholarship feeds commercial AI systems — what she called academic fracking. A literature professor confessed that she once told students to avoid ChatGPT, only to later use it with them to analyze power structures in Victorian novels. A group of students described AI as their learning partner, not a shortcut — proof that the boundaries between tool and teacher are already blurring.

“AI didn’t wake the liberal arts — it found them stirring.”

 

The Human Element: Productive Struggle, Rediscovery, and Redesign

What made the symposium electric wasn’t the technology — it was the humanity pulsing through every discussion. Faculty spoke less about how to control AI and more about how to stay human beside it.

One recurring idea was productive struggle — not as an obstacle to learning, but as its catalyst. AI tools created just enough uncertainty to be generative. Students found themselves asking new kinds of questions: What should I be doing less of? What does originality look like now? How do I make the best use of time with a professor, when the “expert” is increasingly a facilitator of knowledge, not its gatekeeper?

Faculty, too, found themselves in unfamiliar territory. Long-held routines were challenged by tools that could draft, translate, or simulate. The struggle wasn’t about obsolescence — it was about reorientation. What habits of mind are worth keeping? What does rigor mean when the machine can “write” an answer?

And in that discomfort, something vital reemerged: the shared space of learning. Office hours became less about solving and more about sense-making. Less about correctness and more about discernment. Students didn’t need someone to check their work — they needed someone to help them recognize what kind of thinking it was.

In that spirit, the liberal arts reasserted their enduring role — not as defenders of tradition, but as designers of discernment. When algorithms simulate knowledge, discernment becomes the highest art form.

AI may have accelerated this shift, but the liberal arts were always headed there. What emerged across the symposium was a deeper understanding: that growth comes from tension, that rediscovery often begins with unlearning, and that the future of learning may look less like mastery and more like a shared choreography of questioning.

Epilogue: Sora and the Mirror

After the symposium, that metaphor of the mirror stayed with me — especially as I experimented with Sora, a tool from OpenAI that turns words into video. I had received early access just before the conference began. By the time it ended, I had shared all six of my invite codes with colleagues who were curious, eager, and already dreaming up experimental test cases. Invite codes are a fascinating way for software companies to roll things out.

Watching that rollout unfold felt strangely familiar — like history rhyming. Back in 2002, when I was a webmaster in the College of Agricultural Sciences at Penn State, a computer science grad student forwarded me a link to something called Google Beta. “You should check this out,” he said. I did. I joined. And unknowingly, I stepped into something that would transform how the world searches and knows.

Before parting ways, a few of us made videos — short visual essays. You only get 10 or 15 seconds to try out your prompts. A philosopher in conversation with a clone of herself. A student’s dream rendered into shifting light and architecture. A reimagined classroom set in the year 2130. Each piece asked, in its own way: If AI can imagine with us, who decides the shape of the story?

Ultimately, Sora has that same quality — a shimmer of arrival. Something just beginning to shape the future of creation, while reflecting back the questions we haven’t stopped asking.

The technology isn’t just extending imagination. It’s echoing it. It’s reflecting it. And in that echo, it’s asking us what kind of storytellers we want to be.

Conclusion: The Liberal Arts, Awake

By the final plenary, the tone had shifted from anxiety to resolve. The liberal arts weren’t under threat — they were awakening.

The symposium closed not with consensus, but with a shared rhythm: a refusal to let automation define what it means to learn.

Across those three days, AI became less an existential threat and more an existential invitation — not to escape technology, but to wake beside it.

Curious. Critical. And still — profoundly human.

 

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A reflection on the 2025 Connecticut College AI & Liberal Arts Symposium and early experiments with Sora — exploring how AI challenges, reshapes, and ultimately mirrors the soul of liberal arts learning.