AI Education Needs Faculty Development, Report Says
Colleges expanding artificial intelligence education need to develop the faculty who will teach it, according to a Computing Research Association article about a new workshop recommendation report. The report proposes different levels of preparation, hands-on training and institutional support rather than a single program for every instructor.
- The report proposes three levels of AI faculty preparation.
- Training should use active learning and connect to instructors’ disciplines.
- Institutions are urged to provide time, funding, recognition, and resources.
- The recommendations come from workshops involving 39 participants from 38 institutions.
A three-level framework for AI preparation
The report, Designing Faculty Professional Development for Technical and Socio-technical AI, was developed through workshops involving 39 participants from 38 institutions. Its authors include David Touretzky of Carnegie Mellon University, Patrick Gage Kelley of Google, Ella Howard of Wentworth Institute of Technology, Emmanuel J. Dorley of the University of Florida, Roderick L. Lee of Pennsylvania State University, and Sri Yash Tadimalla and Noah Cowit of the Computing Research Association.
Rather than expecting every faculty member to become an AI specialist, the report describes three levels of preparation. Awareness & Critical Recognition covers AI foundations, socio-technical awareness, and basic policy and ethics, and is intended for all faculty navigating an increasingly AI-saturated world.
Application, Management & Pedagogy adds machine learning and generative AI fundamentals, applied pedagogy, bias management, data ethics, and classroom governance. It is aimed at instructors integrating AI into their teaching. Deep Technical & Systems-Level Design covers mathematical and architectural foundations, compute infrastructure, interpretability, complex systems, and high-stakes automation for technical and socio-technical faculty who teach AI.
“It’s not ‘one size fits all’,” Touretzky said. He added that training should create pathways for faculty to develop the technical, socio-technical, and pedagogical expertise they need to teach effectively.
Training should reflect how faculty teach
The recommendations call for active, scaffolded learning that gives instructors opportunities to work directly with AI concepts and tools. Suggested approaches include hands-on activities in Jupyter or Colab notebooks, problem-based learning, case studies, structured reflection and feedback, collaborative course design, and revisiting concepts at increasing levels of depth.
The report also recommends connecting professional development to instructors’ disciplines and teaching contexts. Exercises such as asking “Is this AI or not?” can help establish a grounded understanding, while discipline-specific terms and examples can make the training more relevant to existing courses.
Lee said faculty learn best through active engagement, including opportunities to experiment, collaborate with colleagues, and apply new approaches in authentic teaching settings. The report therefore treats the design of faculty training as part of the teaching challenge, rather than as a separate administrative task.
Sustained support is central to the proposal
A one-time workshop is unlikely to resolve an institution’s faculty capacity challenge, according to the report. It recommends sustained, flexible, community-based development that includes faculty with different technical backgrounds. Possible models include faculty learning communities, cross-departmental communities of practice, and collaborations between institutions that pool expertise and resources.
Institutions could offer multiple entry points, from a one-hour Lunch and Learn to standalone workshops, multi-session courses, teaching and learning centers, online repositories, faculty ambassador programs, and microcredentials. The report also recommends connecting AI development with annual review, promotion, and tenure processes, and providing stipends, course releases, fellowships, course redesign grants, certificates, badges, awards, compute access, and undergraduate or graduate TA/RA support.
Lee said institutions must treat faculty capacity as “a strategic investment” and provide the time, resources, recognition, and support required to make the work sustainable. The report’s broader message is that expanding AI education depends on structures that allow faculty expertise to develop as the field changes. NSF LEVEL UP AI is a collaborative effort involving ACM, CRA, IEEE-CS, AAAI, and other partnering organizations, with work supported by the U.S. National Science Foundation under Grant No. CNS-2434416.
Computing Research Association: You Can’t Scale AI Education Without Scaling the Faculty Who Can Teach It
