AI and Course Design: Machines Can Help, but Only Humans Can Teach

Cue the irony: Students love and actively use artificial intelligence (AI), but they still want humans in charge. A 2025 survey found that while 42 percent of students report using generative AI at least weekly, they overwhelmingly prefer human support when they need help. According to the survey, 84 percent of students said they primarily seek help from a person when struggling with a course concept, compared to 17 percent who primarily turn to generative AI.^Footnote1 Despite the hype and fanfare, students continue turning to faculty they trust for guidance with academic matters and decreasingly use AI to help them understand concepts.

It's clear that AI is reshaping higher education. The technology is no longer knocking on the door. It's already inside, and it's rearranging the furniture. In faculty lounges, curriculum committees, and course design meetings, conversations about AI are urgent, often fraught, and almost always unclear. There's excitement, but there's also fatigue, skepticism, and confusion. Colleges and universities are seeking meaningful and practical ways to engage with the technology; however, most institutions lack a working policy.

At the heart of higher education's response to AI is the vital question of how to harness the technology without sacrificing the humanity of teaching. Because, as it turns out, what students want isn't more automation but more human engagement. And that means keeping people—not technology—at the center of learning.

Faculty are not cogs in an AI-driven machine. They are mentors, architects of learning, and stewards of student success. AI should never replace thoughtful pedagogy or faculty expertise. In fact, its most powerful role is often as a quiet partner that amplifies what educators already do well. When technology is used to empower faculty rather than automate them, it contributes to a more thoughtful, human-centered approach to teaching and learning.

More than thirty years ago, Alison King asked how faculty can shift from transmitting information to helping students construct meaning—from "sage on the stage" to "guide on the side."^Footnote2 In some ways, AI offers a long-awaited chance to fulfill that promise.

To realize this potential, faculty members will need time, space, and collaborative environments in which to experiment, reflect, and iterate. Professional development should feel like a learning opportunity, not a compliance requirement. Despite the common perception that faculty are wary or resistant to AI, many are eager to learn more. When faculty are empowered to explore AI-enhanced design in a supportive fashion, they're more likely to develop lasting, effective practices. They need clarity on why, how, and when to use the technology. Aligning AI tools with clear pedagogical goals is essential.

Whether AI is used to suggest formative feedback, create adaptive assessments, or generate real-world simulations, every use should serve a clearly defined instructional purpose. Without that alignment, there is a risk that AI could complicate rather than enhance teaching. As educator George Couros said, "Technology will never replace great teachers, but technology in the hands of great teachers is transformational." Put more ominously by political scientist Christian Lous Lange, "Technology is a useful servant but a dangerous master."^Footnote3

One promising path forward lies in connecting AI practices to existing quality online learning frameworks, such as Quality Matters. By mapping AI's potential to well-established standards of course design, institutions can give faculty a practical entry point that validates their expertise and preserves academic integrity. Rather than asking educators to reinvent the wheel, institutions can help them fit new tools into the architecture they already trust.

That shift also calls for reimagining quality assurance. Instead of waiting until a course ends to evaluate its effectiveness, AI enables quality checks to be embedded directly into the design process. Imagine a future where faculty receive real-time quality reports aligned with established frameworks, allowing for continuous improvement rather than end-stage triage.

There's a broader narrative that also needs to shift. Too often, AI is framed as a productivity tool for overextended educators to do more with less. In reality, AI has the potential to return time to faculty, rather than consume more of it. By streamlining routine tasks, it can create space for deeper student engagement, renewed focus on research, or simply rest. The extra time AI creates "is yours," writes Matt Miller in his book AI for Educators. "Whether we use it to do something amazing for our students or preserve our mental health, everyone wins."^Footnote4

In a profession increasingly defined by burnout, this reframing is essential.^Footnote5 AI can serve institutional efficiency, but it should also support the well-being of both faculty and students. The old model—expecting every faculty member to do everything—is no longer sustainable. Perhaps it never was.

Ultimately, the rise of AI in higher education is about more than technology. It's about grounding technology in pedagogical purpose, not convenience. It's about empowering faculty through meaningful professional development and embedding quality assurance throughout the design process. It's about establishing clear, student-centered AI policies that build trust. And it's about reframing the narrative. AI isn't here to wring more productivity out of exhausted faculty. It's here to help rehumanize education, if we let it.

Amid all the noise surrounding AI, higher education cannot lose sight of faculty. The future of teaching and learning will be shaped—now as always—by their expertise, values, and leadership.

Notes

1. Catherine Shaw et al., Time For Class 2025: Empowering Educators, Engaging Students (Tyton Partners, 2025), 10, 16.Jump back to footnote 1 in the text.↩
2. Alison King, "From Sage on the Stage to Guide on the Side," College Teaching 41, no. 1 (Winter 1993), 30–35.Jump back to footnote 2 in the text.↩
3. George Courous, "We Need to See Beyond the 'Tool,'George Couros (blog), September 30, 2014; Christian Lous Lange, "Internationalism," Nobel Prize lecture, Stockholm, Sweden, December 13, 1921.Jump back to footnote 3 in the text.↩
4. Matt Miller, AI for Educators (Dave Burgess Consulting, 2023).Jump back to footnote 4 in the text.↩
5. Adrienne Lu, "Faculty and Staff Are Feeling Anxious, Depressed, and Burnt Out, Study Says," The Chronicle of Higher Education, December 3, 2024; The Healthy Minds Study: 2023–2024 Faculty and Staff National Data Report (Healthy Minds Network, October 2024).Jump back to footnote 5 in the text.↩

Deb Adair is CEO at Quality Matters.

Whitney Kilgore is Co-Founder and Chief Academic Officer at iDesign.