Combine bottom-up co-design with top-down executive sponsorship, cross-unit collaboration and dedicated R&D capacity to embed institutional GenAI¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's lessons-learned set for embedding GenAI at institutional scale: bring together diverse experts and facilitate their collaboration; secure senior institutional oversight so innovation aligns with priorities and gains executive awareness; ensure broad institutional buy-in and sharing of lessons; and maintain dedicated R&D units with capacity to explore new pedagogies and technologies outside mainstream operations. The authors report that "Key enabling factors included senior executive sponsorship, cross-unit collaboration between the two R&D units of KMi and IET, Digital Services, and Faculties, and the iterative DBR cycles to refine the functionality of AIDA in response to user feedback."

Design Implications¶

Context¶

Requirements¶

• Dedicated R&D units with staff time for research and development
• Senior executive sponsorship (e.g. steering boards chaired at Pro-Vice-Chancellor level)
• Champions within the organisation and broad staff engagement mechanisms such as communities of practice

Constraints¶

• Diverse expert collaboration is crucial but "not sufficient to embed AI into business as usual"; at the time of writing AIDA's student-facing development had stalled despite these conditions

Target Learners¶

• distance learning students in large higher education institutions

Target Learning Goals¶

• responsible, at-scale institutional adoption of GenAI learning tools

Affordances¶

• Sharples Embedded System Approach Genai

Related Strategies¶

• 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
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Staged institutional implementation: audit interactions, map to teaming levels, build AI literacy, co-design, and evaluate iteratively
• Combine top-down theoretical and bottom-up empirical approaches when developing domain-specific heuristics
• Concentrate AI-edtech adoption priorities through collaborative networks that share tools, policies, and evaluation practices

Examples¶

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Key Sources¶

• Rienties, Bart; Coughlan, Tim; Domingue, John and Herodotou, Christothea (2026). New systems of learning for distance learning institutions? A six-study review of implementing AIDA. Computers and Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2026.100607