PromptResponse #9 - Weekly Insights for AI in Higher Education and the Humanities

Let me tell you what I'm hearing across campus conversations: faculty resistance to AI partnerships isn't really about technology rejection. It's about feeling sidelined in decisions that will shape their teaching, their research, and their students' futures. The professors pushing back hardest are often the ones most invested in academic excellence—they're asking the right questions, and we'd better start treating their concerns as valuable input rather than obstacles to overcome. The three concerns I hear most frequently are control, credit, and consequences. They want to know who's actually making the decisions about how AI tools get used in their classrooms. They want clarity on who owns the intellectual property when AI systems ingest their course materials and research. And they want honest answers about what this means for their roles, their graduate students, and the students they teach. These aren't unreasonable demands—they're the demands of professionals who take their work seriously. Here's what works: bring faculty into the conversation before you've already signed the contract. I've seen universities succeed by establishing faculty advisory committees on AI adoption with real decision-making authority, not just token consultation. Be transparent about the terms—especially the data and IP provisions. And most importantly, acknowledge that AI will change academic work, but frame that change as an evolution of their expertise, not a replacement of it. Your faculty aren't your opponents in this—they're your most credible assets in making AI work for your institution.

Let me be honest with you: the traditional essay is in trouble. When a student can generate a decent draft in thirty seconds using ChatGPT, the five-paragraph theme you assigned no longer tells you what the student can do. But here's the thing—that's actually an opportunity. What AI makes easy is producing text. What it can't replicate is the messy, nonlinear process of thinking through a problem, revising based on feedback, and developing an original argument. That's where our assessment needs to shift. Provenance-based assessment means you evaluate the trail of student thinking, not just the destination. Instead of collecting a final paper, ask students to submit their working documents—early drafts, notes from sources, revision histories, even failed attempts. I'm a fan of what I call the 'portfolio checkpoint': at three points during a major project, students submit a brief reflection on where they are, what they're stuck on, and what questions they're chasing. This does two things. First, it makes their invisible mental work visible so you can actually coach them through it. Second, it creates a provenance record—you can see the evolution of their thinking, which is far harder to fake than a polished final product. The practical part: start small. Pick one assignment this semester and build in a process component worth 30-40% of the grade. Require students to submit their first attempt with explicit mistakes—yes, intentional errors—alongside a memo explaining what they tried and what they're unsure about. Then give feedback on the thinking, not just the writing. When students know you'll reward genuine intellectual risk-taking over polished BS, they'll start taking those risks. That's the pedagogy AI can't automate. The honest challenge: this approach takes more time to assess. You're reading drafts, not just final papers. But here's the trade-off worth making: you're teaching students that learning is a process, that confusion is part of the deal, and that the work behind the grade matters. That's a lesson worth grading.

After three decades of watching university administrators make decisions, I've learned one thing about expertise: it only has value when someone is willing to pay for it. For humanities scholars stepping into consulting, the hardest part isn't finding clients—it's naming your price. The academy trains us to undervalue our knowledge, to see intellectual labor as a calling rather than a commodity. That's a beautiful mindset for tenure-track life, but it's poison for an independent practice. Start with value, not hours. The question isn't "what am I worth per hour«—it's «what is the outcome worth to my client?» A curriculum review that saves a department accreditation headaches is worth far more than the twelve hours you spent on it. When a university hires you to help them navigate a merger or craft a strategic plan, they're buying peace of mind, institutional credibility, and outcomes—not clock time. Price accordingly. Build a tiered structure that creates access points. Offer a diagnostic consultation at a modest rate to lower the barrier to entry, a comprehensive project engagement at your target rate, and an ongoing advisory retainer for clients who want your voice in their decision-making consistently. This approach lets clients self-select based on their needs and budget while protecting your high-value work from being undersold. Finally, test and adjust. Pricing is not set-it-and-forget-it. Survey your clients, track which engagements felt underpriced, and raise your rates annually. The academics who thrive as consultants are those who treat their practice like a business—which means believing that the market rewards expertise, and then having the courage to name the price that proves it.

Here's something that took me a while to learn when I started working with AI tools: the response you get is never the final product. It's a conversation, not a command. The moment you treat AI output as a finished answer is the moment you've missed the point. Those initial responses are rough material—useful, sometimes surprising, but always in need of your editorial eye. The reason this matters is simple: AI doesn't know your specific context, your voice, or your audience the way you do. It can give you a solid structure, suggest angles you hadn't considered, or help you break through writer's block. But it can't replace your judgment. Think of it as working with a smart but eager graduate assistant who needs direction and then needs you to polish what they've drafted. So how do you refine effectively? Start by asking follow-up questions that push the AI to go deeper or adjust tone. Request specific changes: "Make this more conversational" or "Add more concrete examples." Then—and this is the critical part—edit the output yourself. Cut what doesn't fit, add your own insights, and reshape it until it sounds like something you'd actually write. The best results come from this back-and-forth collaboration, not from treating the AI as an oracle. If your first few attempts feel disappointing, that's completely normal. You're not doing it wrong—you're just learning how to steer. Every prompt you refine teaches you something about what works. The writers who get the most out of these tools are the ones who stay engaged, who keep iterating, and who remember that the AI is a tool in their hands, not a replacement for their expertise.