Transparency Mechanisms for Generative AI Use in Higher Education Assessment: A Systematic Scoping Review (2022–2026)

The integration of generative AI in higher education has reignited debates around authorship and academic integrity, prompting approaches that emphasize transparency. This study identifies and synthesizes the transparency mechanisms described for assessment involving generative AI, recognizes implementation patterns, and analyzes the available evidence regarding compliance monitoring, rigor, workload, and acceptability. A scoping review (PRISMA 2020) was conducted using searches in Scopus, Web of Science, ERIC, and IEEE Xplore (2022–2026). Out of 92 records, 11 studies were included, and four dimensions were coded: compliance assessment approach, specified requirements, implementation patterns, and reported evidence. The results indicate limited operationalization: the absence of explicit assessment (27.3%) and unverified self-disclosure (18.2%) are predominant, along with implicit instructor judgment (18.2%). Requirements are often poorly specified (45.5%), and evidence concerning workload and acceptability is rarely reported (63.6%). Overall, the literature suggests that transparency is more feasible when it is proportionate, grounded in clear expectations, and aligned with the assessment design, while avoiding punitive or overly surveillant dynamics. The review protocol was prospectively registered in PROSPERO (CRD420261287226).