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Generative AI seems to be everywhere we look. I’ve seen some universities take incredibly conservative positions over the last year, while others have fully embraced a new era and, of course, many are in between.
As our network of academic innovation officers and units continues to grow, I was curious how generative AI is showing up in the context of these centers, which have increasingly been charged with guiding and supporting a university’s academic strategy through periods of potentially disruptive change.
I spoke to my friend James DeVaney, associate vice provost for academic innovation and founding executive director of the University of Michigan’s Center for Academic Innovation, about a new initiative, how his center is exploring gen AI as a team and where we might go from here.
Q: Your center is often viewed as a prominent change agent in our academic innovation network. Is there an initiative focused on gen AI that you are working on that you find particularly exciting or transformational?
A: Very soon we are going to have an entire catalog of gen AI courses available through Michigan Online. But I should back up for a minute. We spent quite a bit of time over the summer and fall thinking about gen AI in the context of serving our learning community, upskilling our own team and proactively participating in our broader higher education ecosystem. With respect to our learning community—which includes nondegree learners around the world, students in our degree programs and faculty and instructional teams working closely with our center—we’ve been focused on how gen AI might enrich our educational offerings and how to best ensure equitable access to new knowledge and tools. We believe there’s a real opportunity to further strengthen our ability to connect and empower learners everywhere to reach their full potential throughout their lives.
This month we completed a call for proposals encouraging University of Michigan faculty to create online courses designed to “prepare learners for success in an era of artificial intelligence in the workplace.” We are thrilled with the results. We approved 36 new online courses in this round, which we will develop at our new facility in Ann Arbor between now and July. This means that in very short order we will have nearly 50 open online courses available to learners on campus and around the world to help them understand and apply gen AI knowledge and tools and adopt responsible gen AI practices.
In designing this initiative, we specifically sought breadth. We’ve seen several great courses launched in the last few months that provide important overviews and introductions to new tools and technologies, including our own Generative AI Essentials course and courses from institutions like Vanderbilt University and organizations like DeepLearning. Continuing this trend, we will create a suite of introductory courses for everyone. But we are also creating courses to support learners in particular fields like law, music, business and engineering. And we are creating courses for early-career professionals, managers and supervisors, and organizational leaders. Sixteen faculty from eight different colleges are joining us to create this round of courses. Motivated learners and organizations looking to support their employees are in for a real treat.
There is still a lot of important work to do, but we can see a path to a very near future state where learners anywhere from any field can look to Michigan to level up their understanding of gen AI and their ability to make important decisions in the interest of individuals, teams, organizations and society. So to clearly answer your question, given the stakes of supporting human upskilling and workforce development in an age of gen AI, it’s easy to be excited about this initiative. Michigan has consistently created incredibly high-quality online courses. We’re now turning up the dials further on volume, interdisciplinarity and speed to market. If we manage to stay focused, I think creating this empowering space for understanding, shaping and using gen AI responsibly has a real shot at transformational outcomes as well.
Q: It’s great to see that you’re creating an inclusive opportunity for learners to prepare for this new era. What about your own team? How are you preparing yourselves for gen AI?
A: That’s right, the door to the carpenter’s house doesn’t need to be broken. While I would argue that our center and units like it at peer institutions are particularly comfortable with ambiguity and adapting to change, this moment feels different to me. I struggle to find a ping-pong, tug-of-war, whiplash-like metaphor that quite captures the feeling of the last 12 months as the euphoria of one new gen AI use case is replaced by thoughts of its implications and again redirected toward the next use case. It’s exhilarating and exhausting.
Our team is doing its best to learn with and from each other. And the more we do it, the less it feels like our heads are on swivels. Looking inward, we’re asking ourselves, in what ways might gen AI help us streamline our operations? And what new skills do we need to acquire to best anticipate the future of higher education and support our constituents?
Practically speaking, we launched a gen AI task force on a sprint in the fall which led to great insights and several foundational recommendations for our center. Importantly though, it initiated an honest conversation. It doesn’t much matter that we couldn’t exactly say where things were heading. If our mission is to “collaborate across campus and around the world to create equitable, lifelong educational opportunities for learners everywhere,” it’s our obligation to make sense of emerging technologies that impact our ability to best serve our constituents.
Our time-bound task force has since been replaced with a more permanent gen AI council, which includes representatives from each of our teams. Three of our initial recommendations have already been implemented. First, we launched and completed the aforementioned call for proposals. Second, we designed and delivered a gen AI boot camp for our 100-plus staff. At our centerwide all-hands meeting earlier this month, we launched a friendly competition between teams (e.g., learning experience design, marketing, software development, etc.) to develop GPTs to support their regular workflows and responsibilities. We’ll be refining these approaches throughout the semester and putting new ideas into practice.
Third, our operations and policy team has adopted a new review process to carefully evaluate third-party vendors and tools that incorporate gen AI in their offerings. Every company I know has dramatically changed its pitch deck and narrative over the last year. Some actually made substantive adjustments to their products and models, while others have made cosmetic adjustments. Our team needs to be able to tell the difference and also determine relative risk and opportunity in either instance.
As the year unfolds, we will incorporate what we learn together as an organization into our team [objectives and key results] and our individual professional development plans for the year ahead. There are clearly opportunities for each of us to acquire new skills, whether one is primarily an individual contributor, a team supervisor or an organizational leader.
As the leader of this center, I’m neither expressing full-throated optimism nor risk-averse incrementalism. I certainly didn’t see gen AI coming. But it’s clear this is a moment. And our stakeholders have come to depend on our team to provide guidance through periods of change. We embrace experimentation, collect evidence and develop new practices. Gen AI has the potential to extend U-M’s reach and impact. We’ll be in the best position to discern possible, probable and preferred futures if we embrace the (often scary) need to upskill ourselves.
Q: Given the always-evolving landscape of higher education, combined with its traditions and norms, in what ways do you think generative AI will contribute to shaping its future? Where should we go from here?
A: First of all, I love that you referenced “our academic innovation network” in your first question. When we look back over the last decade, our academic innovation peers have initiated and accomplished an incredible amount of positive organizational and ecosystem-level change. We’ve seen new structures emerge and evolve across a range of universities. Networking across universities with folks responsible for similar portfolios was a pretty lonely sport a decade ago. Now I meet a new academic innovation officer on what seems like a weekly basis.
So while, sadly, I don’t have a crystal ball, I do know how to prepare. And preparing with friends turns out not only to be more fun but yields better results.
I can think of three “what does it all mean” moments from the last decade or so. We welcomed the year of the MOOC in 2012, and for certain stakeholders within higher ed, it provided the excitement that comes from relaxing constraints and reconsidering what’s possible. In 2020, people everywhere were rocked by the pandemic, which provided the feelings of doom that come from societal trauma and reconsidering what might be taken away. At present, we are jolted by gen AI, which has been compared to electricity and air, and people across higher ed and people everywhere are torn between reconsidering what’s possible and what might be taken away.
As I think about where we should go from here, I start with where I hope we’ll go. I assume we want gen AI to improve teaching and learning, improve the ways we work and collaborate and improve our lives. But we can’t assume this will just happen, right? We had a prominent ed-tech CEO join us for a conversation on campus recently, and he said he kind of wished we could just pause for a year to make sense of what’s happening. Not the response we’ve come to expect from the press-ahead ed-tech community.
But can you blame him? This feels like an epic trust fall at a company retreat, except the retreat is attended by absolutely anyone on the street who wants to show up, no one explains the rules before you’re leaning backward off the edge of the raised platform and you’re left wondering who or what is going to decide whether to catch you.
Gen AI currently looks like it could have an impact on everything, so, again, without a crystal ball, I’ll focus from my particular vantage point in higher ed. We need to experiment carefully, we want to ensure equitable access and we want to contribute to a standard of responsible AI use across higher education. This calls for more targeted grant funding focused on applications in teaching and learning. It calls for more strategic and collaborative partnerships between industry and higher ed. And it calls for rapid standardization of contractual terms and policies that promote responsible AI practices.
Apologies that my responses are neither brief nor were they packaged as haiku. I couldn’t bring myself to edit with ChatGPT. For better or worse.
