Establish comprehensive institutional AI governance frameworks including task forces, roadmaps, and transparent policies¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The brief recommends that postsecondary leaders develop comprehensive AI usage guidance, including establishing an AI task force to set standards for data privacy, security, bias, and accuracy, and an institutional roadmap with phased initiatives and ongoing monitoring. It advises that institutions "clarify regulations for the permissible uses of AI, including how learners can properly cite AI use or disclose the use of AI, as well as when AI use is prohibited," plus accountability measures and inter-institutional collaboration.

Design Implications¶

Context¶

Requirements¶

• A governing body to develop and evaluate standards for data privacy, security, bias, and accuracy of AI tools
• Public and transparent institutional vision to gain buy-in

Constraints¶

• Implementation is described as a long-term commitment, with learners arriving with varied skill sets for many years

Target Learners¶

• Postsecondary educators
• Postsecondary students
• Institutional leaders and policymakers

Target Learning Goals¶

• Responsible and ethical AI use
• Understanding institutional AI policy and permissible uses

Related Strategies¶

• Set clear AI guardrails positioning AI as a complement to human instruction with age-appropriate progressions
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Four strategic recommendations for supporting AI literacy across settings
• School districts adopting AI should follow a five-step roadmap: engage communities, apply and develop policies, provide professional development, create supportive learning environments, and monitor and evaluate AI use
• Foster communities of practice for AI governance instead of rigid top-down policies
• Legislators should pass consumer-protection legislation and create a regulatory agency governing AI education products
• Use the Recommendation's Policy Action Areas to translate AI ethics values into policy across education, data governance and other spheres
• Adopt AI incrementally in waves from simple to complex tasks, on free-tier tools, with mandatory multi-step human review before any document is issued
• Build institutional AI literacy and transparent risk-communication governance for agentic AI browsers
• Adopt a roadmap for ethical CAI implementation in education
• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection
• Strengthen institutional policies, ethics education, verification, disclosure, and data protection to support ethical GenAI use
• Disclose quantization trade-offs, audit bias, and establish data governance before live deployment of quantized educational AI
• Use the taxonomy as a self-assessment and strategic-planning roadmap for institutional AI adoption
• Staged institutional implementation: audit interactions, map to teaming levels, build AI literacy, co-design, and evaluate iteratively
• Adopt ethical safeguards and pedagogically grounded frameworks when deploying GenAI assessment tools
• Prioritise longitudinal validation, pedagogical grounding, governed adoption of agent infrastructures, and ethics with human oversight in educational agentic AI research
• Institutional safeguards for safe, transparent AI use in assessment
• Deploy agentic AI in education with transparency about agentic decisions, human oversight and institutional responsibility
• Deploy soft barriers transparently: institutional approval, policy-level disclosure, and accessibility testing

Examples¶

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Key Sources¶

• Zachariah, I., Peters Hinton, V., Ruiz, P., Dallas, D., Liberman, B., & Karim, S. (2025). AI Literacy in Postsecondary Education. Digital Promise. https://doi.org/10.51388/20.500.12265/270