Operationalising AI governance in higher education: A stepwise policy-development method

The institutional adoption of generative artificial intelligence (AI) requires governance methods that turn use-specific risks into implementable decisions, safeguards, responsibilities and review routines. Existing frameworks provide normative guidance, but few operationalise governance at the level of individual AI-use scenarios. This article presents the Higher Education Artificial Intelligence Policy Development Method (HE-AIPD), a stepwise method that enables cross-functional teams to develop institutional AI-use policies. HE-AIPD converts AI-use evidence into a reusable governance package comprising decision rules, risk safeguards, policy clauses, role responsibilities, implementation tools and a review cycle. The method also includes a traceability design linking use cases to decisions, clauses, roles and tools. Content validity was assessed through an expert walkthrough with 20 participants using a 4-point scale across 12 components and 5 criteria, yielding an overall S-CVI/Ave of 0.882. Operationalises institutional AI governance by converting AI-use scenarios into decisions, safeguards, clauses, roles, tools and review routines. Provides reusable governance outputs: scope sheet, use-case inventory, decision matrix, risk-safeguard map, clause bank, implementation toolkit and review cycle. Includes reproducible content-validity calculations based on expert walkthrough data deposited in Mendeley Data.