A Framework for Generative AI in Graduate Professional Development

Most AI policies focus on coursework and academic integrity; professional development contexts remain largely unaddressed. Faculty and career advisers need practical strategies for guiding students to use generative AI critically and effectively. This article proposes a four-stage framework—explore, build, connect, refine—for guiding students’ generative AI use in professional development.

Over the past decade, graduate education has invested significantly in career readiness through dedicated offices, individual development plans and co-curricular programming—for example, the Council of Graduate Schools’ PhD Career Pathways initiative involved 75 U.S. doctoral institutions building data-informed professional development, and the Graduate Career Consortium, representing graduate-focused career staff, grew from roughly 220 members in 2014 to 500-plus members across about 220 institutions by 2022.

These investments reflect recognition that Ph.D. and master’s students pursue diverse career paths, with fewer than half of STEM Ph.D.s entering tenure-track positions immediately after graduation; the figure for humanities and social sciences also remains below 50 percent over all.

We now face a different challenge: integrating a technology that touches every part of the knowledge economy.