Nadeera Ahangama
Abstract The rapid expansion of generative artificial intelligence (GenAI) as a disruptive force in modern times presents tertiary education with both unprecedented opportunities and profound challenges for assessment design. As GenAI can create plausible assessment responses with limited student engagement, many traditional assessments are now at risk of becoming obsolete. Assessment reform is particularly important in ICT programs, where GenAI can readily generate assessment content, such as coding, and algorithms, and technologically savvy students may be prone to misuse it rather than engage in authentic learning. Consequently, sector-wide discussions are centred on how to redesign assessment to enable the appropriate and ethical use of GenAI while ensuring students genuinely achieve the expected learning outcomes. Despite calls for updated strategies from quality assurance bodies, a significant gap remains between the pace of GenAI advancement and the implementation of such assessment reforms. The objective of this study is to identify how educators can design assessments for ICT tertiary students that accurately measure student learning outcomes in an era of widespread access to GenAI tools. Using a qualitative interpretivist approach and thematic analysis of scholarly and institutional practice documents, this research proposes the GENESIS framework (GenAI-Enabled Strategic and Instructional System for Assessment), a five-pillar, institution-wide model emphasising shifts in institutional values, governance, and support as prerequisites for sustainable assessment reform. GENESIS is complemented by the Assessment Design Matrix (ADM), a tripartite structure integrating graduate competencies, assessment strategies, and assessment methods. ADM provides a practical, hands-on guide for assessment designers to craft integrity-focused assessments that assess ICT graduate competencies. Together, GENESIS and ADM propose a structured, discipline-tailored framework to ensure assessments continue to validly measure student learning within the GenAI ecosystem.