Support faculty AI literacy through needs assessment, targeted training, teaching resources, and communities of practice¶

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

Description¶

The chapter treats faculty development as crucial to implementing the framework. Institutions should first "assess faculty AI literacy needs through a multi-faceted approach," including surveys of current AI knowledge and teaching practices, syllabus analysis to identify gaps in AI coverage, and faculty focus groups. Based on these assessments, institutions can offer workshops on AI technologies, expert-led seminars on ethical considerations, and hands-on training, plus comprehensive integration guides, online forums, brown bag sessions, and mentorship pairing AI-experienced faculty with newcomers.

Design Implications¶

Context¶

Requirements¶

• Institutional investment in faculty training, resource development, and ongoing support structures

Constraints¶

• The chapter notes resource constraints in funding and expertise, and possible faculty resistance to change, as primary implementation barriers

Target Learners¶

• faculty and instructional staff in higher education

Target Learning Goals¶

• faculty capacity to integrate AI literacy across the curriculum

Claims¶

• Educators report needing training, resources, and institutional support to introduce educational change [+W]

Related Strategies¶

• Embed AI literacy across disciplines by identifying intersections with existing content and using case studies, interdisciplinary modules, and collaborative projects
• Foster communities of practice for AI governance instead of rigid top-down policies
• Supports to Raise Teachers' Expectancy and Value and Lower Cost for AI Use
• Invest in ongoing educator professional development and an assessment community of practice for POG success
• Embed AI policy literacy, peer-led communities of practice, and shared AI resources into professional development
• Preparing preservice teachers to implement AI-powered personalized learning in special education through professional development in technical and pedagogical expertise
• Build teacher AI readiness through structured institutional support: professional development, mentoring, technical assistance, and AI literacy in teacher education
• Prioritize sustained, pedagogically grounded faculty TPACK development over one-off technology workshops and infrastructure investment

Examples¶

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Key Sources¶

• Kathleen Kennedy & Anuj Gupta. (2025). AI & Data Competencies: Scaffolding holistic AI literacy in Higher Education. Thresholds Volume 48, Issue 2. https://journals.ku.edu/thresholds