Scaffolded AI-Verification: Assessment Patterns for Resource-Constrained Environments.
The widespread availability of generative tools has weakened a long-standing assumption in computing education: that the production of working code can serve as a proxy for student competence. In resource-constrained settings, these tensions are compounded by intermittent power, high data costs, and emergent institutional governance. We report a two-site qualitative study of Nigerian computing departments (n = 20), drawing on semi-structured interviews with students and academic staff and analysing the corpus through thematic analysis to characterise assessment practice under policy-light conditions. Our findings describe a persistent detection trap, where staff rely on inconclusive software or subjective judgement, and institutional silence, where expectations for acceptable use are unevenly specified and applied. We contribute the Scaffolded AI-Verification Framework (SAVF), presented as a traceable design pattern catalogue of handset-first, low-data feasible teaching moves derived from these stakeholder accounts. SAVF comprises (i) permitted-help statements with disclosure, (ii) process-evidence bundles that foreground explanation and testing, and (iii) course-anchored prompts that require adaptation to local materials and constraints. We provide three pattern specifications, a traceability table linking themes to requirements and patterns, and adoption guidance for low-bandwidth implementation, positioning SAVF as a stakeholder-informed design contribution with a testable evaluation plan for future in-situ study rather than as an evaluated intervention.