2026 ASEE Annual Conference & Exposition

Building the Plane While Flying It: Results of Roundtables to Increase Capacity in Tertiary AI Education

Presented at CIT Technical Session 5: Curriculum and Program Development.

Many institutions are currently grappling with teaching artificial intelligence (AI) in the face of growing demand and relevance in our world. Undergraduate institutions of all types and sizes are currently facing a capacity challenge in AI education, with shared problems and opportunities arising from this continuously developing disciplinary area. This paper discusses the results of 32 moderated virtual roundtable discussions of 202 experts committed to improving AI education, as part of a national project to advance AI education at undergraduate institutions throughout the United States. These discussions slot into four focus areas (1) AI Knowledge Areas and Pedagogy, (2) Infrastructure Challenges in AI Education, (3) Strategies to Increase Capacity in AI Education, and (4) AI Education for All. Roundtables were organized around institution type (e.g., R1, R2, Only Undergraduate, Community College, etc.) to consider the particular goals and resources of different tertiary AI education environments. We synthesized the thoughtful and informed roundtable discussions of experts in this report, both with respect to general AI education and to the particular needs of different institutions and positionalities. Together, these summarize the practices, challenges, and strategies institutions and individuals are exploring to improve AI education. In particular, we identified the following high-level community needs to increase capacity in AI education. A significant digital divide creates major infrastructure hurdles, especially for smaller and under-resourced institutions. These challenges manifest as a shortage of faculty with AI expertise, who also face limited time for reskilling; a lack of computational infrastructure for students and faculty to develop and test AI models; and insufficient institutional technical support. Compounding these issues is the large burden associated with updating AI curricula and creating new AI programs. To address the faculty gap, accessible and continuous professional development is crucial for faculty to learn about AI and its ethical dimensions. This support is particularly needed for under-resourced institutions and must extend to faculty both within and outside of computing programs to ensure all students have access to AI education. Further recommendations include specific curricular and community-based solutions. To streamline learning pathways, launching an Applied Math for AI course could avoid several burdensome prerequisites, while embedding ethics throughout the AI curriculum is essential. Pedagogically, using in-class assignments can provide valuable insight into student learning processes. Creating faculty learning communities can foster a supportive AI educational ecosystem and can bolster professional development, while establishing student clubs and makerspaces can provide vital, less formal environments for AI exploration and learning. We hope that our detailed findings and resources will lead to a robust and informed discussion on how to best serve our students as we adapt to the rapidly changing space in computing education to create a workforce of AI specialists.

Authors

Noah Q. Cowit Computing Research Association [biography]

Noah is currently a CI AI Education Fellow at the Computing Research Association (CRA) in Washington, DC, where he is contributing to national efforts to define AI learning goals and scalable models for undergraduate education. He serves as the lead Fellow for the NSF LEVEL UP AI project, which aims to establish consensus on the articulation of the AI knowledge areas, identify the enabling infrastructure needed for expanding high-quality AI education, and develop strategies for increasing AI education capacity.
Sri Yash Tadimalla University of North Carolina at Charlotte [biography]

Sri Yeswanth (Yash) Tadimalla holds a Ph.D. in Computing and Information Systems with an interdisciplinary focus in Sociology and a Master of Science in Computer Science from UNC Charlotte. A Bachelor of Technology in Computer Science and Engineering from the Hyderabad Institute of Technology and Management. He currently serves as a Computing Innovation (CI) Fellow focused on AI Education at the Computing Research Association (CRA) in Washington, DC. At CRA, he is supporting research and outreach across multiple NSF-funded initiatives and serves as the lead Fellow for the NAIRR Pilot Expansion AI EDU RCN. Dr. Tadimalla actively contributes to various global multilateral organizations, including the United Nations, through thought leadership, coordinating consultations, and producing policy reports, with a specific focus on civil society engagement, education, and innovation within negotiations around AI governance and capacity building. He has presented his work as a keynote speaker and researcher at distinguished international conferences (ASEE, ACM-SIGCSE, IEEE-FIE, AAAI, UN) across diverse global locations in 20+ countries.
Dr. Stephanie T. Jones Computing Research Association [biography]

Stephanie T. "DJ" Jones is an AI Education Fellow with the Computing Research Association. As an interdisciplinary researcher they focus on the ethical design and use of technology in learning environments, addressing how research infrastructures can challenge or uphold structural inequities. DJ earned her Ph. D. in Computer Science and Learning Sciences and an M.S. in Computer Science from Northwestern University. She has a B.S. in Computer Engineering with a minor in Computer Science and a B.S. in Electrical Engineering from Villanova University.
Mary Lou Maher http://orcid.org/0000-0002-4150-0322 University of Sydney [biography]

Mary Lou Maher is a Professor in the School of Computer Science at the University of Sydney. Her research and teaching focus on AI in education and co-creativity.
Enrico Pontelli New Mexico State University
Dr. Tracy Camp Computing Research Association [biography]

Tracy Camp is a Full Professor and Head of the Department of Computer Science at the Colorado School of Mines. She is the Founder and Director of the Toilers (http://toilers.mines.edu), an active ad hoc networks research group.

Her current research interests include the credibility of ad hoc network simulation studies and the use of wireless sensor networks in geosystems. Dr. Camp has received over 20 grants from the National Science Foundation, including a prestigious NSF CAREER award. In total, her projects have received over $20 million dollars in external funding. This funding has produced 12 software packages that have been requested from (and shared with) more than 3000 researchers in 86 countries (as of October 2012). Dr. Camp has published over 80 refereed articles and 12 invited articles, and these articles have been cited almost 4,000 times (per Microsoft Academic Search) and over 7,000 times (per Google Scholar) as of December 2012.

Dr. Camp is an ACM Fellow, an ACM Distinguished Lecturer, and an IEEE Fellow. She has enjoyed being a Fulbright Scholar in New Zealand (in 2006), a Distinguished Visitor at the University of Bonn in Germany (in 2010), and a keynote presenter at several venues, e.g., at the 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2011) in Adelaide, Australia, and the 3rd International Conference on Simulation Tools and Techniques (SIMUTools 2010) in Malaga, Spain. In December 2007, Dr. Camp received the Board of Trustees Outstanding Faculty Award at the Colorado School of Mines; this award was only given five times between 1998-2007.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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