Tag Archives: Transdisciplinarity

Beyond AI-Proofing: Designing for Integrity, Fluency, and the Future of Liberal Arts Learning

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A colleague and friend who teaches at a liberal arts college in California recently shared with me that she spent in the ballpark of 40 hours last summer redesigning a research paper assignment to be “AI-proof” in her fall seminar. At the end of spring semester, her students will graduate into jobs that require them to use AI daily. We are teaching students to hide the very literacy their future demands.

The call to “AI-proof” our assignments is reverberating through higher education — from faculty lounges to academic technology roundtables. It’s urgent, it’s sincere, and it’s fueled by genuine pedagogical care. And honestly? Much of it is brilliant. Faculty are innovating at a rapid pace, reimagining assignments to protect authenticity and preserve intellectual honesty in a world where generative AI writes, summarizes, and simulates with astonishing speed. These AI mitigation strategies aren’t born of paranoia. They’re born of craft — an earnest desire to keep learning human.

The Pedagogical Craft of Mitigation

Educators have been remarkably inventive in this space, creating multi-layered strategies that do more than block AI — they build discernment. Here’s a look at the some of them, their power, their limits, and their impact on student learning:

Table comparing eleven AI mitigation strategies in higher education.

 

These approaches certainly work. Some better than others, and combining them can be powerfully effective. Click to see examples of strategies in practice. They slow down the learning process, foreground reflection, and reward originality. They represent the best instincts of liberal arts education — the impulse to protect learning as a relational act, not a transaction.

But here’s the paradox: the more time we spend perfecting these defenses, the further we drift from the future we’re supposed to be preparing students for.

The Problem with Perfecting Resistance

We’re designing moats when we should be building bridges.

As faculty, we’ve become experts in preventing students from using the very tools they’ll need to thrive beyond graduation. While we construct sophisticated “AI-proof” systems — password-protected PDFs, multi-phase proctoring, closed platforms — the professional world is racing ahead, expecting graduates who can think with AI ethically, effectively, and creatively.

We are, unintentionally, teaching students a skill that has no future application: how to learn without the tools they’ll be required to use for the rest of their lives.

The deeper problem extends beyond individual assignments. When faculty independently prune “AI-able” work without departmental coordination, the result is curricular chaos:

  • Duplicated efforts across courses
  • Gaps in skill progression
  • Students experiencing five different AI policies across five courses in their major

This work cannot be done in isolation. It requires departmental conversation about shared outcomes, scaffolded skill development, and coherent AI policies across the major.

When “Integrity” Turns Into Invasion

Surveillance is not pedagogy.

There’s a line we shouldn’t cross: installing surveillance software on student computers. That doesn’t teach integrity; it broadcasts distrust. It says, “Your laptop — your window to creativity, exploration, and daily necessity — is now a controlled asset I can monitor at will.” If that’s our definition of academic integrity, we’ve already surrendered the idea of education as a partnership.

And we know where this logic can spiral: from “protect the test” to “police the person.” History shows how quickly tool-level monitoring becomes life-level monitoring. It’s a short walk from integrity to intrusion.

Some respond, “Fine — go back to pen and paper.” Honestly, that’s less dystopian than spyware. But let’s not romanticize blue books. For many students, English (or any academic register) isn’t their first expressive mode — and most of us actually learned to write by typing. Picture the blue-book era: you discover your real argument halfway through, realize the perfect sentence belongs three paragraphs up, and you’re frozen in ink. No cursor. No drag. No re-ordering. No thinking-through-revision — the essence of writing itself. You start drawing arrows, crossing out paragraphs, performing neatness while throttling cognition.

And outside a surgical theater, almost no profession rewards “compose your best argument by hand in 40 minutes while a clock screams at you.”

So yes — if the choice is creepy spyware or smudged ink, I’ll take ink over intrusion. But both miss the point. Neither reflects how people actually think, write, collaborate, verify, or revise in 2025. Both are control systems — one analog, one algorithmic — aimed at the wrong target.

Liberal Arts, Not Lie Detectors

The moral center of teaching is trust.

At bottom, the surveillance classroom sends one message: we don’t trust our students. The liberal arts should do better than that. We’re meant to be the standard-bearers of inquiry, dialogue, and moral imagination. If the best we can offer is dashboards and suspicion, we’ve traded away our pedagogical soul.

I say this with deep respect for colleagues doing heroic work — often under pressure, often while fielding understandable anxiety from administrators, parents, and even their own instincts to protect what matters most about education. These concerns are real. The fear is legitimate. We’re witnessing a paradigm shift unfolding before our eyes and in our classrooms, and we desperately need every perspective at the table to navigate it thoughtfully. The AI-resistant strategies you’ve built are evidence of care, craft, and commitment to authenticity. That work matters. Your voice matters.

But panic is not a strategy. And control is not pedagogy.

The way forward isn’t to out-police our students; it’s to out-design the problem. If our energy goes into designing around distrust, we’ll starve the very habits of mind we claim to teach. Design for evidence of learning, not evidence of catching. Trust as default, transparency as practice, rigor as design. That’s how the liberal arts lead.

The Shift: Performance as Pedagogy

From catching to coaching.

Here’s what changed my thinking: watching professionals in action — including myself.

In my work, I don’t submit written reports to prove what I know. I present. I facilitate. I respond to questions I didn’t anticipate. I think on my feet, synthesize in real time, and demonstrate understanding through dialogue and improvisation. That’s how the professional world actually assesses expertise — not through pristine documents composed in isolation, but through performance: the ability to explain, adapt, defend, and collaborate under pressure.

Why aren’t we teaching that?

If we want integrity without surveillance and rigor without nostalgia, change the mode of evidence. Make learning observable, human, and grounded in the communication skills the world actually values.

Design for performance, not policing:

  • Oral assessments — brief, coached defenses that make reasoning visible
  • Video essays — planned, revised, reflective storytelling with sources and documented process
  • Live presentations with Q&A — synthesis under light pressure, supported by artifacts
  • Recorded demonstrations — show the build, the test, the fix; narrate the decisions

These aren’t just “AI-proof”; they’re future-proof. They develop the soft skills employers actually demand: clear communication, adaptive thinking, grace under uncertainty. They teach improvisational, situational leadership — the ability to demonstrate what you know when someone asks a question you didn’t prepare for.

And here’s the bonus: in designing these performance-based assessments, we’re also teaching technological literacy. Students learn video editing, audio production, visual storytelling, digital composition — the multimodal fluencies that define 21st-century communication. Each iteration gives them practice. Each presentation builds confidence.

This is how I learned to show what I know. This is how your students will need to show what they know.

Want inspiration? Talk to your campus teaching, learning, and technology center. They’re already piloting these approaches. They have tools, templates, rubrics, and pedagogical frameworks ready to support you. You don’t have to reinvent this alone.

From here, the path forward becomes clear: make learning too specific, too process-visible, too human to fake.

The Transdisciplinary Turn: From Resistance to Responsiveness

The question isn’t “Should students use AI?” The question is “How do we teach them to use it well, critically, and humanely — within our disciplines?”

That’s the transdisciplinary challenge now facing every liberal arts curriculum. It’s not just a question for computer science or writing programs — it’s a shared design problem spanning philosophy, biology, studio art, and sociology alike.

An AI-responsive curriculum embraces both sides of the coin:

  • AI Resistance ensures cognitive integrity — the ability to think unaided, to wrestle with ideas, to claim one’s voice
  • AI Integration ensures cognitive fluency — the ability to think with tools, to discern when to trust them, and to synthesize machine assistance with human judgment

Neither is optional. Together, they form the new liberal art: technological self-awareness — the capacity to understand not just what we know, but how we come to know it alongside intelligent systems, and what remains distinctly, necessarily human in that process.

What AI Literacy Looks Like in Practice

A responsive curriculum asks students to:

Document their AI use as part of their process — showing how the tool informed, shaped, or misled their work.
Biology example: Generate a preliminary literature scan with AI; verify each citation; identify misrepresentations; reflect on what the AI got wrong about recent research methodology.

Reflect on the ethics of automation within their discipline — what’s lost, what’s gained, what must remain human.
Philosophy example: Prompt AI to construct an ethical argument; use course readings to identify logical gaps, hidden assumptions, or misapplied concepts; turn the AI’s output into the object of analysis itself.

Evaluate AI outputs for accuracy, bias, and context — building critical reading and synthesis skills across modalities.

Integrate multimodal expression — text, image, sound, video, data — to demonstrate learning that transcends the written word and develops the communication fluencies their futures demand.

Engage in meta-learning — understanding not just what they know, but how they came to know it alongside intelligent systems.

This is what AI literacy in the liberal arts should look like: a blend of philosophical questioning, technological discernment, creative practice, and performative demonstration.

A Call to the Faculty

The hard work of AI literacy doesn’t fall on students. It falls on us.

We’re the ones who must rethink assessment, let go of some control, and reimagine academic integrity not as suspicion but as shared inquiry. We can’t expect students to navigate this complexity ethically if we aren’t modeling how.

I’m sensitive to the constraints. I see the pressures — departmental, institutional, accreditation-driven. Many of you are teaching overloads, navigating budget cuts, fielding impossible demands. I know some of you are skeptical, exhausted, or both. That’s valid. This is hard work, and it requires support, time, and institutional commitment that isn’t always there.

But I also believe this: the liberal arts — with their long tradition of self-reflection, interdisciplinarity, and humanistic questioning — are exactly where this reimagining must begin. We’ve always been the ones asking not just what to teach, but why and how. That’s our strength. That’s our calling.

The Future We Should Be Building

All those AI-resistant strategies? Keep them. They’re valuable. They’re proof that faculty care deeply about authenticity and intellectual honesty. But don’t stop there.

Pair them with the equally essential work of AI fluency — teaching students to engage, critique, and co-create with intelligent systems. Add performance-based assessments that make learning visible, human, and grounded in the communication skills the world actually demands.

Because the future of education won’t belong to those who can simply resist AI. It will belong to those who can work wisely with it — and demonstrate that wisdom through voice, presence, and adaptive thinking.

So here’s a challenge to us:

This semester, design one AI-resistant assignment. Next semester, design one that teaches AI fluency and requires students to perform their learning — through presentation, video essay, oral defense, or live demonstration. Compare what you learn from each. Share your findings with colleagues. Coordinate as a department. Connect with your teaching and learning center. Experiment together. Build coherence.

Because the real work isn’t deciding whether AI belongs in our courses — it’s deciding what kind of intelligence we’re teaching students to cultivate, and what kinds of humans we’re helping them become.

When our assignments are too human to fake and our learning too authentic to outsource, we will have done more than “AI-proof” education.

We’ll have future-proofed it.

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.