Staged institutional implementation: audit interactions, map to teaming levels, build AI literacy, co-design, and evaluate iteratively¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The chapter recommends institutions translate the teaming framework into practice as a staged, capacity-building process rather than a technical deployment challenge. "A practical starting point is for institutions to audit existing teacher-AI interactions within their platforms and workflows and map them onto the five levels of teaming to identify their current baseline." This mapping then guides design decisions such as upgrading transactional tools towards situational or operational teaming, structured professional development aligned with AI literacy frameworks, human-centred co-design with teachers, and robust evaluation cycles using process analytics and teacher reflections.

Design Implications¶

Context¶

Requirements¶

• Institutional capacity for professional development aligned with AI literacy frameworks such as the UNESCO AI CFT, and human-centred design processes involving teachers

Constraints¶

• The chapter states these discussions remain at an early stage in GenAI development and its pedagogical and ethical integration, highlighting the need for future empirical investigation

Target Learners¶

• teachers
• school and institutional leaders

Target Learning Goals¶

• teacher AI literacy
• pedagogically aligned GenAI adoption

Affordances¶

• Five Levels Teacher Ai Teaming Framework

Related Strategies¶

• Establish comprehensive institutional AI governance frameworks including task forces, roadmaps, and transparent policies
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Integrate co-design and continuous feedback loops into AI evaluation processes
• Embed anticipatory governance in institutional strategy via Chief AI Officers, AI task forces, faculty AI-literacy development, and student co-design mechanisms
• Foster communities of practice for AI governance instead of rigid top-down policies
• Use the DOT Framework's Empathize and Test/Feedback stages as practical checklists, and build professional learning communities around shared prompting and critique practices
• Staged readiness strategy: establish policy clarity and faculty capacity-building before ambitious AI-enabled or online scale-up
• Combine bottom-up co-design with top-down executive sponsorship, cross-unit collaboration and dedicated R&D capacity to embed institutional GenAI
• Deploy GenAI for continuous longitudinal analysis of student development to enable ipsative feedback, and scaffold feedback literacies for all students

Examples¶

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Key Sources¶

• Mutlu Cukurova, Wannapon Suraworachet, Qi Zhou, and Sahan Bulathwela. (2025). Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence. https://arxiv.org/abs/2511.19580