As generative AI becomes ever more pervasive in society, business, and in higher education, there are increasing calls for colleges and universities to ensure that students are AI-ready when they enter the workforce. But organizations have different visions of what AI readiness actually entails. These frameworks include, to some degree, a set of AI-related skills that students need to more successfully enter the world of AI-inflected work. Ambiguity remains, however, around what skills employers expect of recent graduates. This in turn complicates any attempt to understand if colleges and universities are sufficiently preparing students for these expectations.

To bring some clarity and specificity to this space, Ithaka S+R conducted a national survey of undergraduate instructors about what AI-related skills they prioritize and which, if any, they actively teach in their courses. We compared these data with results from 200 US-based employers collected by HiBob, a multi-national human resources technology company, to find areas of alignment or dissonance. The AI Skills Framework consists of 26 specific skills across seven broader categories, including AI literacy; AI safety, ethics, and governance; and automation and technical integration. This direct exploration of instructor and employer priorities on the same set of AI-related skills offers one of the first direct comparisons between instructors and employers views of the specific skills that may constitute the so-called AI skills gap.

What we found

Instructors placed the highest priority on skills related to responsible use of AI, the critical evaluation of outputs, and maintaining human accountability while employers prioritized these skills to a lesser degree. Employers placed a much higher priority on skills related to workflows and automations than did instructors. In line with these priorities, instructors indicate that they are not teaching many of these lower priority skills in their courses. This pattern aligns with the differing broad missions of higher education institutions and workplaces, namely cultivating critical thinking and disciplinary content knowledge vs. organizational efficiency and productivity.

The full report provides a detailed comparison of the instructor and employer prioritizations within the AI Skills Framework, along with the full list of categories, individual skills, and their detailed definitions.

What’s next

In addition to this specific set of skills, we also asked instructors what else they would prioritize for their students’ AI-related learning, where they turn for their own AI-related professional development, and who on campus should be responsible for teaching students about AI-related academic integrity and how to discuss their AI skills with employers. We will be publishing follow-up blog posts exploring these topics and the possible implications for the field.