Developing Strategies to Increase Capacity in AI Education

2025-09-25

AI Summary

Addressing systemic bottlenecks in AI education—including faculty shortages, insufficient computational infrastructure, and outdated curricula—this study draws on empirical data from 202 experts across diverse higher education institutions. Employing virtual roundtable discussions, thematic analysis, and interdisciplinary modeling, the research proposes a scalable, quality-enhancing strategy. Its key innovations are a cross-institutional resource-sharing mechanism and a sustained faculty development framework—both representing the first systematic integration of AI knowledge frameworks, infrastructure provisioning, pedagogical resources, and public dissemination pathways. The study has facilitated the establishment of a national AI education public resource repository, systematically cataloging and openly sharing over 100 high-quality educational assets. The resulting open-access, implementation-ready guidelines empower universities to significantly improve educational capacity and equity in AI instruction.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Addressing infrastructure hurdles and faculty shortages in AI education
• Developing accessible professional development for AI curriculum updates
• Creating centralized repository of AI education resources for institutions

Innovation

• Virtual roundtable discussions with 202 AI experts
• Identified infrastructure hurdles and faculty development needs
• Proposed central repository for accessible AI education resources