Institutions should respond to GenAI with clear governance frameworks and structured professional training programs for staff.¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 2 studies (1 review, 1 qualitative), q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

The article recommends institutional-level responses to staff survey findings: clear governance frameworks for GenAI use and structured professional training programs for educators. The authors state that "there is an urgent need for clear governance frameworks, structured professional training programs," positioning these as urgent institutional priorities arising directly from staff concerns about integrity, equity, and uncertainty about responsibilities.

Design Implications¶

Context¶

Requirements¶

• Institutional commitment to governance and professional development capacity for GenAI in learning and teaching.

Constraints¶

Target Learners¶

• University academic staff
• Higher education students

Target Learning Goals¶

• Responsible and pedagogically sound use of generative AI in learning and teaching

Claims¶

• Engineering faculty report reluctant engagement with GenAI, concerns about cognitive offloading, and frustration with uncoordinated institutional policy [+W]
• Fewer than 10% of teaching staff and students in surveyed Arab higher education institutions had received formal AI training [+W]

Related Strategies¶

• Foster interdisciplinary collaboration and actively include student perspectives in institutional GenAI responses.
• Foster communities of practice for AI governance instead of rigid top-down policies
• Higher education leaders should develop more inclusive and future-oriented GenAI policies integrating social equity, interdisciplinary experimentation, and sustainability considerations
• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Develop clear institutional GenAI policies, communicate them, raise awareness, and promote digital literacy
• Strengthen institutional policies, ethics education, verification, disclosure, and data protection to support ethical GenAI use
• Shift teacher professional development from information-based training to experience-based AI design activities in sustainability contexts
• Design GenAI governance and cross-role contact around integrity disputes, the principal site of sustained faculty-student contact
• Use findings on instructor power relations to inform professional development for responding to Generative AI
• Accrediting bodies and professional organizations should provide AI policy frameworks integrating academic integrity, data ethics, and equitable access, and programs should invest in faculty development
• Implement preventative measures: student awareness programs and regular professional development training for educators

Examples¶

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Key Sources¶

• Enright, H., Horvath, D., Petrovic, K., & Šarkić, B. (2026). “AI Should Help them Learn, Not Learn for Them”: University Staff Perspectives on the Role of Generative AI in Education. Journal of University Teaching and Learning Practice. https://doi.org/10.53761/d5n2yh02