What Competencies Do Faculty Need in Regard to AI?

Faculty competency should extend well beyond prompt writing. The knowledge base's AI Literacy synthesis identifies four broad dimensions:

• Foundational understanding of AI capabilities and limitations.
• Practical competence using AI tools.
• Critical evaluation of accuracy, bias, appropriateness, and uncertainty.
• Ethical and institutional awareness involving issues such as integrity, privacy, equity, and AI Governance.

The pedagogical layer

For instructors, those dimensions need an additional pedagogical layer. Faculty should be able to:

• Decide what cognitive work students must retain.
• Select AI uses that align with learning objectives.
• Design valid AI-era assessment.
• Teach students to verify and regulate AI use.
• Recognize hallucination, overreliance, and sycophancy.
• Know when human judgment should override automation.

Faculty also need enough systems understanding to evaluate how generative AI-supported workflows fit together rather than viewing AI as an isolated tool. See Teacher AI Competency and Faculty Development.

Confidence is not competence

Research summarized in the knowledge base also cautions against equating confidence with competence: self-reported AI literacy can diverge substantially from demonstrated capability, making performance-based professional development and authentic practice preferable to confidence surveys alone. Teacher multi-agent-workflow research further suggests that systems thinking, pedagogical beliefs, and Self-Efficacy interact in AI integration, so professional development should be differentiated rather than a one-size-fits-all tool workshop.

What professional development actually moves — and what it does not

Recent higher-education research puts concrete numbers on where faculty competency is strong and where it breaks down. A survey of 127 faculty at a large U.S. research university using the Technological Pedagogical Content Knowledge (TPACK)-21 instrument found self-perceived pedagogical content knowledge high (M = 4.70) and content knowledge near-ceiling (M = 5.15), but the technology-integrated domains markedly lower — technological pedagogical knowledge (M = 2.62), technological content knowledge (M = 2.75) and holistic TPACK (M = 2.55, the lowest of seven). Content expertise did not predict GenAI-integration knowledge (CK correlations r = .11–.15, all non-significant), so faculty development cannot assume subject-matter mastery transfers and should build generative AI competence through deliberate, discipline-specific programming. Because the three technology-integrated domains correlated r = .81–.91, they may function as a single GenAI-integration capacity worth teaching as a shared foundation rather than as separate skills.

What training can and cannot do is equally instructive. In a within-subjects study with 13 higher-education teachers, giving teachers a chatbot significantly improved the quality of their learning design — the share attaining two or more higher-order Bloom tasks rose from 41.7% to 91.7% (p = .01) — and sharply lowered perceived cognitive effort (median 6.33 to 3.33, p = .004); the pedagogical prompting training layered on top did not further improve quality and slightly raised effort. AI access, in other words, changes teaching behavior readily; deeper capability takes more than a workshop, and quality gains alone do not prove teachers internalized the practice.

Standards, agency and identity

Competency is also an institutional question. Design-based research with 114 participants across 28 U.S. institutions and 11 countries produced six faculty technology standards spanning the full academic role — Instructor, Coordinator, Leader, Researcher, Learner and Contributor — because existing frameworks (Technological Pedagogical Content Knowledge (TPACK), SAMR) and educator standards (ISTE, UNESCO, DigCompEdu) address K-12 teachers or only the teaching dimension.

A survey of 287 teachers across 27 countries shows why adoption resists a purely technical framing: the refined model explained 79.8% of the variance in behavioral intention, yet perceived artificial autonomy influenced intention only indirectly through usefulness, and risk aversion was a significant negative predictor (−.163), with teachers worrying far more about students' overreliance and integrity risks than their own use. Adoption depended on context-sensitive judgment rather than the appeal of automation. Finally, interviews with two experienced academics suggest AI integration generates recurring identity tensions — pedagogy versus platform, educator versus facilitator, care versus compliance — navigated through principled selectivity grounded in pedagogical values. Competency therefore includes professional judgment about when not to use AI, not only skill in using it.

For the student-facing counterpart, see How Should I Incorporate AI Literacy into My Course? and What Is the Evidence on AI Literacy Interventions in Higher Education?; for the instructor-facing summary, see What Are the Top 10 Findings from AI in Education Research That Instructors Should Know About?.