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 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 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.