College instructors and employers have strikingly different views on which AI skills matter most for graduates, according to a new study that surveyed 500 professors and compared their priorities to those of 200 U.S.-based employers. The research, conducted by Ithaka S+R in partnership with HiBob, a human resources technology company, reveals a critical misalignment that could leave students unprepared for the AI-driven workplace they're about to enter.

The study examined 26 specific AI-related skills organized into seven broad categories: AI literacy, continuous learning orientation, prompting and input quality, evaluating and improving output quality, AI safety and ethics and governance, workflow evaluation and redesign, and automation and technical integration. These categories reflect how typical non-technical employees actually use AI tools like large language models (LLMs), which are AI systems trained on vast amounts of text data to generate human-like responses, in their daily work.

What Skills Are Instructors Actually Teaching?

The research surveyed 500 college instructors from four-year nonprofit institutions during the spring 2026 academic term, asking them to rate the importance of each AI skill and whether they personally taught it in their courses. The instructors represented a diverse range of disciplines and institutional types. Nearly 60 percent worked at doctoral institutions, while fewer than 12 percent taught at exclusively undergraduate colleges. The majority identified as tenured or tenure-track faculty, though a substantial number held adjunct or contingent positions.

Notably, some instructors expressed skepticism about AI in higher education itself. Several survey recipients responded directly to decline participation, citing concerns about AI in general or AI in educational settings. One instructor suggested that knowing "how to disable and resist AI is the most important skill," while another argued that "the skill of doing things without AI" should be prioritized for college graduates. These perspectives highlight an important tension in higher education as institutions rush to integrate AI into curricula.

Where Do Employers and Instructors Disagree?

The study compared instructor responses to data from HiBob's separate survey of 1,200 employers conducted in February and March 2026, with 200 of those employers based in the United States. This comparison reveals where educational priorities diverge from workplace demands. The research identifies specific points of alignment and dissonance between what colleges are teaching and what employers say they need when hiring workers who will use AI tools.

Understanding these gaps matters because the stakes are high. As artificial intelligence becomes embedded in more jobs across industries, the skills gap between what graduates learn and what employers expect could disadvantage early-career workers. Students who graduate without the skills employers prioritize may struggle to demonstrate competency in AI-related tasks, even if they've completed AI coursework.

How to Bridge the AI Skills Gap in Higher Education

• Align curriculum mapping: Colleges should conduct a detailed audit of their current AI-related course content and compare it directly against employer priorities identified in studies like this one, then adjust learning outcomes accordingly.
• Involve employers in curriculum design: Universities can invite industry partners to participate in curriculum committees or advisory boards to ensure that course content reflects real-world workplace demands and evolving AI tool usage.
• Emphasize practical evaluation skills: Rather than focusing solely on how to use AI tools, instructors should dedicate time to teaching students how to critically evaluate AI outputs for quality, bias, and accuracy before using them in professional contexts.
• Integrate ethics and governance early: AI safety, ethics, and governance should be woven throughout the curriculum rather than treated as a standalone topic, helping students develop responsible AI practices from the start.
• Create continuous learning mindsets: Since AI technology changes rapidly, colleges should prioritize teaching students how to learn independently and adapt to new tools, rather than focusing on mastering any single platform.

The research comes at a critical moment. Since ChatGPT's public release in 2022, both higher education institutions and employers have focused intensely on preparing students for AI-inflected work. Many colleges and universities have rushed to provide access to enterprise AI platforms or integrate AI into their curricula, sometimes despite student and instructor concerns. Local, state, and federal policymakers have also begun offering guidance on how educational institutions should prepare students for the age of AI.

However, the fundamental question remains unclear: what does it actually mean to be ready for the AI workplace? While there is enormous collective attention on students' and workers' skills with using AI, it is less clear what "AI skills" should encompass. AI skills are as new to employers as they are to graduating students, and the technology itself is changing so quickly that many may struggle to remain on the cutting edge.

The Ithaka S+R study represents one of the first major efforts to vet a comprehensive framework of what constitutes AI skills with college and university instructors and to understand how they prioritize these skills. By combining data on how employers and instructors view the skills in the framework, the research identifies where institutions and instructors could better align their curricula, assessments, and learning outcomes to employer priorities.

The findings underscore a broader challenge in higher education: the pace of technological change often outstrips the pace of curriculum development. Establishing a specific, actionable, and assessable framework for what constitutes AI skills is a necessary first step in aligning efforts to sufficiently support students as they transition from higher education to employment in the age of AI. Without this alignment, colleges risk graduating students who feel confident in their AI abilities but lack the specific competencies employers are actively seeking and willing to pay a premium for when hiring.