Generative AI Competency Assessment Strategies and Industry Employability Expectations in Higher Education: A Systematic Review

In this systematic review, the alignment between generative artificial intelligence (GenAI) competency assessment strategies in higher education and industry skill expectations for graduate employability is analyzed. Following the PRISMA 2020 guidelines and the SPIDER framework, a search was conducted across Scopus, Web of Science, and ERIC, resulting in the inclusion of 26 empirical studies. The analyzed studies document the use of strategies focused on process rubrics, iterative prompt engineering cycles, and critical curation. In the industrial sector, employers point to requirements focused on expert human supervision, algorithmic bias auditing, and data governance. Regarding the alignment between academic skill development and corporate requirements, the studies indicate varying levels of convergence, registering gaps in continuity regarding deep technical application, ethical regulations, and contextualized task design. The theoretical foundations in evaluative design range from classical constructivist models to emerging approaches. In conclusion, the literature points to a nuanced alignment landscape. While academic assessment and industry requirements overlap regarding critical output supervision, operational gaps remain in regulatory data governance and enterprise workflow orchestration.