Three months. We had just three months to design a brand-new program that would bring together over 50 faculty representing each department and school at our university to provide professional learning opportunities about generative AI in teaching and learning to their peers. Our provost’s office tasked our Center for Teaching Excellence with developing the program, recruiting and hiring faculty, preparing these faculty to provide learning opportunities to their colleagues, and providing ongoing support for these faculty. We needed to take the Faculty AI Guides program at the University of Virginia from idea to launch in just three months.
No problem.
Higher education is no stranger to disruption. The COVID-19 pandemic. Generative AI. The war in Gaza. The current U.S. presidential administration. And that’s all just in the last five years. These institution-level disruptions affect, and affect differently, how instructors teach and how students learn across the disciplines. Responding well to the challenges posed by these disruptions requires leaning into our institutional teaching missions, responding from our core identity as educational institutions, and leveraging the strengths of our faculty, staff and students. And if your institution has a center for teaching and learning (CTL), that unit is likely well positioned to be a key leader in any institutional response to a great disruption.
Our university’s response to generative AI provides an example of how CTLs can lead institutional change efforts. Since the jump scare that was the release of OpenAI’s ChatGPT in the fall of 2022, faculty have looked for support in facing the challenges (and opportunities) that AI brings to their teaching. Yes, policy statements from administration and technical primers from AI experts have been useful, but it has been expert guidance on teaching and learning from trusted colleagues that has done the most to help faculty adapt and adjust to the current AI moment. Our CTL had the pedagogical expertise, relationship networks, and built community trust to provide this kind of support and to do so quickly.
Like many institutions, the University of Virginia initially responded to generative AI with information sessions for faculty and the formation of an AI task force. It was evident from those early conversations that some initial reactions, like banning AI under the incorrect assumption that AI use by students could reliably be detected, were insufficient. What was needed was a more robust exploration of the affordances and limitations of generative AI for student learning that was grounded in specific disciplinary and pedagogical contexts.
Starting in the spring of 2023, our center has offered a variety of AI-related programming and resources, from teaching workshops and faculty learning communities to students-as-partners initiatives and on-demand web guides. These efforts brought instructors from across disciplines into conversation about AI’s role in the classroom and provided opportunities to share myriad experiences of teaching with, about, and against AI. The Faculty AI Guides program, developed in those busy three months between May and August 2024, illustrates how the Center for Teaching Excellence (CTE) leveraged improvisation, collaborative networks and meaningful support to serve as a “center for resiliency” (Kaplan, Wright, & Bruff, 2024) in the university's response to generative AI.
Improvisation
The Faculty AI Guides program features a number of components adapted from existing CTE approaches to faculty development. Our faculty fellows program informed the train-the-trainer approach we used to support the AI Guides. Our experience designing and implementing high-engagement workshops for faculty meant we could put together a two-day institute for the new AI Guides in short order. We regularly explore teaching topics through faculty learning communities, so we adapted that structure to support the AI Guides in monthly small-group meetings. And our experience assessing faculty development efforts meant we could regularly collect data on the program as it unfolded, informing in-the-moment course corrections and longer term programming.
We brought to this work faculty-like expertise in teaching and learning and professional development that the AI Guides program needed, along with a staff-like ability to take on a significant new project not quite at the drop of a hat, but rapidly enough to provide the institution-level response needed for this disruption. Those who work at CTLs are, in a sense, professional shapeshifters. No one in the CTE had context-area expertise in AI before the fall of 2022, nor do any of us currently specialize solely in AI. However, we could readily adapt our expertise in course design, educational technology, the scholarship of teaching and other areas to the work of supporting faculty exploration of AI.
Collaborative Networks
Few campus units rival CTLs in their collaborative and professional networks. CTLs often function as a “hub” (Wright, 2023), convening and connecting faculty, staff and students across an institution. Our CTE leveraged its network to launch and support the Faculty AI Guides, starting in the recruitment phase of the program, which saw over 100 applications in just a few weeks over the summer. Many of the AI Guides had participated in other high-engagement offerings from the CTE, like our nationally known Course Design Institute, and so were already well-versed in evidence-based teaching practices. (They also knew what they were getting into by signing up for a CTE program!) The program, and particularly its two-day kick-off institute, involved a range of existing CTE campus partners, including instructional technologists, librarians and writing-across-the-curriculum specialists. We also leveraged our network of CTL faculty and staff at other institutions. As skilled as we are, we don’t have expertise in every domain, so we learn from our peers in the profession.
One important feature of the Faculty AI Guides program is that the provost’s goal for the program wasn’t to have all faculty adopt generative AI in their teaching but to make informed and intentional choices about AI that respond to their teaching goals and contexts. The faculty fellows were called “AI Guides” and not “AI Ambassadors” on purpose. That provost-level goal was aligned with how CTLs do their work, not prescribing particular teaching practices but helping faculty and other instructors think critically about their own teaching. Had that alignment not been there, we would have had a harder time bridging what can sometimes be a gap between administration and faculty priorities.
Meaningful Support
For CTLs to be successful leaders of change initiatives, they must have meaningful support from their institutions. For CTE, this support has come in very tangible ways, including financial resources and provisions for a large and stable center staff. That support was critical for the rapid launch of the Faculty AI Guides program, but so was less tangible support, like access to information and trust in the CTE to do the work well. This support didn’t appear overnight. Indeed, the trust that university leaders place in the CTE was built over many years of productive collaboration and strategic alignment of CTE goals with university priorities. Also critical was the trust that faculty across the university have in the CTE to be reliable and useful partners in pedagogical change, trust that was also earned over time.
In these days of tight and tighter budgets, academic leaders can feel they aren’t in a position to build or expand a center for teaching and learning. Doing so, however, is a very practical way to invest resources in an institution’s teaching mission. And even in the absence of new funds, there are ways leaders can support CTLs. The work of faculty development benefits from relationships with faculty built over time, so institutions can consider ways to retain and promote CTL staff. Academic leaders can also make sure CTLs are at the table for strategic planning conversations. The CTE’s director, Michael Palmer, was part of that original AI task force, which meant the CTE and its strengths could be part of institutional responses to the AI challenge.
There’s a belief that we sometimes hear that a center for teaching and learning is mostly a resource for instructors who want to make their lectures more dynamic. We can certainly help with that, but as the Faculty AI Guides program at the University of Virginia shows, a CTL can also effectively lead an institution-wide response to a disruption as significant as generative AI has been these last few years. We are excited about the improvisation and networking that has already come with the second year of the Faculty AI Guides program. We're hoping for a third year, but we probably won't get a greenlight for that until next year's budgets are more settled. Odds are, we won't have a lot of time to retool the program at that point.
No problem.
