The AI Pit Stop for Faculty

“The Pit Stop” is the first post in a ten-part series. Each short post addresses one key design question for higher education in relation to the AI shock. Taken individually, most of these proposals are fairly uncontroversial, and versions of them are already being implemented here or there. Taken together, however, the ten points sketch the outline of a school that does not yet exist. 1. Training the trainers Teaching students about AI and its consequences requires faculty members who are able to keep up with both. This sounds like a no-brainer. Translating it into institutional practice is far from obvious. Many faculty members have not yet had sustained, structured professional exposure to generative AI, especially when it lies outside their domain expertise. Faculty members are hired based on their academic credentials: their PhD, publications, and research record, or, in vocational and teaching-oriented schools, on their professional and pedagogical merits. For anyone beyond the very beginning of their teaching career, ChatGPT-like AI was not part of the picture when they started. Formal continuing professional development programs are not always built into academic routines, and there is a good reason for this. Faculty members keep up to date by the very nature of their jobs. Research-active faculty contribute to advancing the knowledge frontier in their own domains. A generic training program can therefore feel redundant, or even slightly out of place. Practice-oriented faculty remain in close contact with relevant professional communities through a variety of channels. They often retain a professional activity on the side, attend professional events, exchange with practitioners, and continue learning through self-directed practice. Under this model, academia relies on a decentralized diffusion process. New knowledge and practices emerge from research and professional communities. Faculty members absorb them, interpret them through their disciplinary expertise, and translate them into new course content and learning activities. AI, however, is creating a mismatch between the speed of technological change and the speed of academic diffusion. At the individual level, postponing serious engagement with AI for a year or two may be an entirely reasonable decision. AI may not be central to a faculty member’s current research, teaching priorities, or professional practice. At the institutional level, however, the accumulation of such reasonable decisions can produce a serious outcome: entire cohorts may graduate without any structured opportunity to examine how AI is changing their field. The problem is therefore not that faculty members are failing to do their jobs. It is that a decentralized diffusion mechanism designed for slower and more localized changes may be insufficient for a technology whose impact is broad, deep, systemic, and rapid. Training faculty about AI does not mean asking them to embrace or promote it, nor to converge on a single view. Critical, skeptical, and resistant positions are an integral part of informed teaching. The goal is informed judgment on AI grounded in direct exposure, practice, and reflection. One response would be to establish a recurring faculty-development system: a common baseline for all faculty members, discipline-specific practice, regular updates, peer exchange, and practical support for redesigning courses and assessments. This is more interventionist than the usual decentralized model of academic development. But it reflects the full scale of AI’s impact: broad, deep, systemic, and rapid, at a much larger scale than previous technological shocks on education. In France, NEOMA Business School offers interesting examples: a school-wide AI development program for students, faculty, and staff, as well as a workshop and online course for teachers in French preparatory classes. How should such faculty development be organized? A pit-stop model Faculty members are rightly focused on their domains of expertise. They have limited time to devote to activities that may initially appear peripheral to their teaching or research. AI faculty development should therefore look less like another degree program and more like a pit stop: short, intensive, recurring, and connected to ongoing support. A. Recurring AI and its ecosystem keep evolving at such a rapid pace. Here is an account, from my recent experience. An annual intensive session could provide an institutional reset. Given the pace of change, however, it should probably be considered a minimum rather than a complete system. Shorter updates during the year and an ongoing exchange channel could help faculty remain connected between pit stops. B. Critical distance AI training may be perceived as an exercise in technology advocacy. It should therefore be made explicit that these programs are intended to enable informed judgment across the full spectrum of possible positions: adoption, selective use, refusal, regulation, and outright criticism. The models, tools, key actors, costs, limitations, and social consequences of AI are changing rapidly. Arguments and recommendations that were well founded one year ago may need to be reconsidered the next. C. Match the program to faculty needs For the training to be effective, it must address the diversity of its audiences. A useful starting point would be three pathways: Foundations: for faculty members who rarely or never use AI. The focus would be discovery, contextualization, core concepts, potential uses, limitations, costs, and risks. Applied practice: for regular users who want more structured development. The program would cover a range of tools and use cases, with practical exercises related to academic work. Research-related and teaching-related activities should be treated separately. Advanced experimentation: for experienced users interested in pushing the envelope. The format would be closer to a masterclass or peer laboratory, centered on emerging workflows, advanced tools, and the design of new pedagogical practices. The people leading these sessions should combine technical currency, pedagogical credibility, and disciplinary relevance. Internal faculty practitioners should play a central role, supplemented where useful by external experts. D. Recognition and workload Faculty members know that their craft is changing: assessment methods, classroom pedagogy, research practices, and sometimes the knowledge of their discipline itself. The time they invest in faculty development should be formally recognized as part of their workload and professional development. A certificate or badge may be useful, but recognition matters more than credentialism. E. Follow-up An ongoing community of practice should connect faculty members and facilitators between pit stops. This could include a discussion channel, short updates, shared experiments, office hours, and a repository of discipline-specific examples. Why this is foundational When thinking about the impact of AI on higher education, I cannot think of a more foundational first step than training the trainers. This foundation enables and multiplies the impact of all the initiatives that will follow, which I will examine in the next posts in this series. In the meantime, I would especially like to hear about faculty-development initiatives you have led or participated in. What is recurring? What is practice-based? And what has actually changed in courses or assessments as a result? About Me I lead higher-education programs in interactive design and video games at Gobelins Paris, and I develop independent web applications. I created Nocode Functions and SlideLang. Try them out and let me know what you think. I’d love your feedback! The views expressed here are my own and do not necessarily reflect those of my employer or any organization with which I am affiliated. Email: analysis@exploreyourdata.com Bluesky: @seinecle Blog: Read more articles on AI, coding, and higher education.