# Developing Strategies to Increase Capacity in AI Education

arXiv: https://arxiv.org/abs/2509.21713 · HTML: https://mlsanity.com/paper/2509.21713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [The AI Index 2021 Annual Report](https://mlsanity.com/paper/2103.06312/) (2021)
- [HighRES: Highlight-based Reference-less Evaluation of Summarization](https://mlsanity.com/paper/1906.01361/) (2019)
- [Challenges and Solutions in AI for All](https://mlsanity.com/paper/2307.10600/) (2023)
- [AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum](https://mlsanity.com/paper/2409.10552/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors](https://mlsanity.com/paper/2606.12428/) (2026)

## Abstract (arXiv)

Many institutions are currently grappling with teaching artificial intelligence (AI) in the face of growing demand and relevance in our world. The Computing Research Association (CRA) has conducted 32 moderated virtual roundtable discussions of 202 experts committed to improving AI education. These discussions slot into four focus areas: AI Knowledge Areas and Pedagogy, Infrastructure Challenges in AI Education, Strategies to Increase Capacity in AI Education, and AI Education for All. Roundtables were organized around institution type to consider the particular goals and resources of different AI education environments. 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 curricula and creating new 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. We have compiled and organized a list of resources that our participant experts mentioned throughout this study. These resources contribute to a frequent request heard during the roundtables: a central repository of AI education resources for institutions to freely use across higher education.