From Permission to Pedagogy: The Structured AI-Guided Education Assessment Policy (SAGE-AP) for Generative AI in Higher Education

Higher education policy on generative artificial intelligence has developed rapidly, yet much of this development remains stronger on governance, permission, disclosure, and assurance than on pedagogy. Universities increasingly move beyond blanket prohibition by distinguishing between restricted and permitted contexts, requiring acknowledgement of tool use, and introducing verification mechanisms to protect authorship and understanding. However, publicly visible institutional approaches appear less developed in providing structured, student-facing workflows that guide responsible AI engagement during assessment completion. This article, informed by a bounded qualitative document analysis, uses the term pedagogical middle layer to describe the process guidance needed between institutional permission settings and academic-integrity or misconduct procedures. Drawing on recent literature and a purposive scan of selected publicly available university policy and guidance documents, the paper argues that current public-facing models are often effective at defining boundaries but less explicit in guiding disciplined, transparent, and defensible forms of human–AI collaboration. In response, the paper presents the Structured AI-Guided Education Assessment Policy (SAGE-AP) as a theoretically grounded policy proposal for AI-assisted assessment, rather than as an empirically validated policy intervention. SAGE-AP frames assessment as a staged process in which students begin from their own understanding, engage with AI critically, document evaluative decisions, refine outputs responsibly, and defend the reasoning represented in the final submission. The paper contributes to institutional policy development by clarifying how permission settings may be complemented by pedagogical process guidance in the generative AI era.