Abstract

This semi-systematic review investigates the literature on generative AI (GenAI) in higher education from 2023 to 2025, with a particular focus on its use for assessment purposes among teachers. Using a targeted search strategy across Web of Science, Scopus, and ERIC, an initial pool of 53 articles was filtered down to five studies that met the inclusion criteria. The review originally aimed to explore structural relations of GenAI adoption in teacher assessment practices, examining how current research addresses the integration and impact of these technologies in educational settings. The selected studies predominantly feature higher education teachers, and most mix both teacher and student participants. Notably, none of the reviewed models or frameworks focus specifically on assessment or evaluation; instead, they adopt a holistic approach to GenAI usage and acceptance. The findings reveal a lack of targeted research on assessment-related applications of GenAI, with most studies addressing broader themes such as general adoption. This highlights a significant gap in the literature regarding the specific relational mechanisms and outcomes of GenAI-focused assessment practices. The review underscores the need for future research to develop and evaluate models that explicitly address assessment and evaluation. Closing these gaps is crucial for understanding and maximizing the potential of teachers’ use of GenAI in assessment and for determining if a redesign of existing approaches is urgently needed.

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