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AI Skills for College Graduates: How Instructors and Employers Prioritize AI Skills Differently

● high · 2026-08-19 · ai-literacy, curriculum-design, higher-ed, workforce, generative-ai, professional-training, teacher-education

Ithaka S+R's AI Skills for College Graduates report surveys 500 US four-year-college instructors (April 2026) on how they prioritize 26 AI-related skills from the HiBob AI Skills Framework, and compares their ratings to a panel of US employers surveyed by HiBob. The headline finding is a systematic skills-prioritization gap: instructors and employers agree on the importance of only one skill (setting realistic expectations for AI-augmented work), with instructors favoring a critical, responsible-use orientation (limits of AI, attribution, human accountability) while employers favor workflow, automation, and human–AI teaming skills. Instructors also report their institutions lack both a consensus on what AI skills look like and an assessment framework for them — an infrastructure gap that parallels AI Literacy debates.

Background and the AI Skills Framework

The report is one of the first major efforts to vet a comprehensive AI-skills framework with college instructors. The framework, developed by HiBob (a multinational HR technology company) to understand which AI skills employers seek and pay a premium for, spans seven categories and 26 skills:

  1. AI literacy — understanding human vs. AI capabilities, selecting appropriate tools, setting realistic expectations
  2. Continuous learning orientation — iterating on AI tools, peer coaching
  3. Prompting and input quality — clear actionable prompts, grounding in reliable sources, Multimodal prompting
  4. Evaluating and improving output quality — proactive review, revising drafts for audiences, resisting sycophantic AI, bias & fairness awareness
  5. AI safety, Ethics, and Governance — handling sensitive data, responsible use, human accountability, transparency & attribution
  6. Workflow evaluation and redesign — mapping workflows, efficient human–AI handoffs, documenting decisions
  7. Automation and technical integration — no/low-code automations, basic coding/API capabilities

The framework is designed to capture how a typical, non-technical employee uses AI tools at work — from foundational LLM chatbots to purpose-built systems.

Methodology

  • Instructor survey (Ithaka S+R): 500 instructors from four-year nonprofit colleges responded (28,200 invited, April 13–24 2026). They rated each of the 26 skills on a seven-point importance scale, reported whether they taught each skill, and answered questions on institutional AI context.
  • Employer data (HiBob): 1,200 employers surveyed Feb–March 2026; the report compares instructors against the US subset (200 employers).
  • Respondent profile: mostly tenured/tenure-track (300) plus 122 adjunct/contingent; ~three-fifths at doctoral institutions and public universities; disciplines led by social sciences, natural sciences, and business.
  • Limitations: the report notes potential early-response bias, exclusion of community colleges/vocational institutions, and that the "skill of doing things without AI" was a recurring instructor refrain.

Key Findings

A wide alignment gap, with one point of agreement

Across the 26 skills, instructors and employers agreed on the importance of only one: setting realistic expectations for AI-augmented work (an AI-literacy skill). In most other cases one group rated skills higher than the other.

Instructors prioritize responsible, critical use

Instructors ranked highest the skills reflecting responsible use of AI and navigating the limits of human and AI capability: uses AI responsibly, reinforces human accountability, transparency & attribution, proactively reviews output quality, and recognizes the limits of one's own expertise. One instructor noted that "understanding the limitations of AI and the fact that LLM cannot replace human intelligence is key." These priorities align with foundational academic values — attribution, review and revision, and information literacy.

Employers prioritize workflow, automation, and teaming

Employers valued skills related to workflows, automation, and efficient human–AI collaboration — the activities of workers rather than students. They also highly valued human-to-human and human-to-machine skills more than instructors, reflecting the team-oriented, efficiency-driven nature of workplaces compared to the individualistic evaluation common in academic settings.

The teaching gap is the strongest evidence of a skills gap

Only three of 26 skills are taught by half or more instructors. Two-thirds teach transparency & attribution (likely via academic integrity), and roughly half teach responsible use, proactive output review, and human accountability. But far fewer instructors teach the workflow-evaluation and technical-integration skills — precisely the categories where instructor–employer prioritization diverged most. The report calls this "the strongest evidence that there is an AI skills gap between higher education and employers": whole categories employers value are neither prioritized nor taught.

Institutional context: expectations exceed infrastructure

Instructors slightly disagreed that their institutions expect undergraduates to acquire moderate AI proficiency, and more strongly disagreed that institutions have a consensus on what AI skills look like or a shared framework to assess them. One instructor reported their "institution announced AI literacy as a top priority—and then did nothing since." These findings indicate institutions are in the early stages of organizing around AI-ready learning environments.

Implications for AI in Education

  • For curriculum design: The report offers a concrete, assessable 26-skill framework institutions can use to map existing coursework against employer priorities — a practical antidote to the "vaporware" vagueness of AI-skills talk. It highlights specific under-taught categories (workflow redesign, automation, technical integration) that many institutions could add without abandoning critical-use values.
  • For assessment: A shared framework for assessing AI skills is a prerequisite for knowing whether students reach proficiency; the report documents that most institutions lack this, echoing Assessment debates in the wiki.
  • For AI Literacy: The instructor emphasis on critical, responsible use aligns closely with the wiki's treatment of AI literacy as regulatory competence and critical thinking, while the employer emphasis on productivity-oriented skills introduces a complementary framing the field is still reconciling.
  • For equity and practice: The report flags the presence of instructors who see resisting/doing-without AI as the key skill — a framing tension relevant to Framing AI Use For Students and Student Experience.

Connected Concepts

Connected Articles

Citation

Fried, M. (2026). AI skills for college graduates: Exploring how instructors and employers prioritize AI skills differently. Ithaka S+R. https://sr.ithaka.org/publications/ai-skills-for-college-graduates/