Faculty professional development treating AI literacy as core professionalism with critical, ethical, and pedagogical emphasis¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 qualitative), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends that institutions conceptualize AI literacy development as a core dimension of faculty professionalism rather than a merely technical skill. It states: "Professional development programs should emphasize critical understanding, ethical reasoning, and pedagogical innovation in AI use", aligning with UNESCO's view that AI competency underpins equitable, human-centered digital transformation. Related recommendations include multi-layered institutional support combining infrastructure with emotional and professional support, and well-being-centered AI governance.

Design Implications¶

Context¶

Requirements¶

• Institutional policies that prioritize multi-layered support systems combining infrastructural investment with emotional and professional support

Constraints¶

• The authors note pathways' relative strengths are context-dependent: in centralized systems institutional infrastructure may be the primary catalyst, while in more liberal systems interpersonal collaboration may matter more

Target Learners¶

• University faculty; higher education leaders and policymakers

Target Learning Goals¶

• Faculty AI literacy, ethical reasoning about AI, and pedagogical innovation with AI

Claims¶

• Faculty concerns centered on academic integrity (80%) and student over-reliance on AI (70%), and their most requested development topic was AI ethics and academic integrity (70%) [+W]

Related Strategies¶

• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Ground AI integration in higher education on core principles of human agency, academic freedom, transparency, ethics, inclusivity, and critical thinking
• Build teacher AI readiness through structured institutional support: professional development, mentoring, technical assistance, and AI literacy in teacher education

Examples¶

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Key Sources¶

• Liu, W.; Li, Y.; Yan, Y.; Wang, J.; Wang, J.; Zhang, H. (2026). Exploring University Faculty’s AI Well-Being: A Structural Equation Model of Social Supports, AI Literacy, and Technological Self-Efficacy. Behavioral Sciences. https://doi.org/10.3390/bs16071168