Designing Faculty Professional Development for Technical and Socio-technical AI: NSF LEVEL UP AI Workshop Recommendations

Abstract

As demand for AI education increases, higher education institutions face the challenge of offering a full range of AI courses to all interested undergraduate students. Addressing this challenge requires a workforce of educators capable of teaching both the rigorous technical foundations of machine learning and the complex sociotechnical frameworks necessary for its responsible training, deployment, and use. However, many institutions report a shortage of faculty with this specialized expertise. This strain is compounded by a competitive labor market where industry frequently poaches top talent, leaving academic departments to rely on overextended staff. This document highlights strategies for implementing professional development in AI instruction for existing faculty, covering (Section 1) important topics of training, (Section 2) useful pedagogical strategies, (Section 3) potential structures of development, and finally (Section 4) incentives for faculty to not only attend but be fully motivated to upskill their ability to teach AI.