Mapping generative AI in higher education teaching and learning: a scoping review

Generative artificial intelligence (GenAI) has rapidly entered higher education, but empirical research on its roles in teaching and learning remains fragmented across instructional activities, outcomes, assessment approaches, and stakeholder perspectives. This scoping review followed PRISMA-ScR guidelines to map 394 empirical studies of GenAI use in higher education published from November 2022 through December 2025. The review examined how GenAI tools were integrated into authentic teaching and learning activities, what outcomes or skills were targeted, how studies assessed those outcomes, and how students and instructors perceived GenAI use. Across the reviewed studies, GenAI was most often used to support existing academic tasks, especially feedback, tutoring, and writing. The most frequently targeted outcomes included engagement, usability and acceptance, critical thinking, disciplinary knowledge, academic performance, writing quality, and AI literacy. Self-regulated learning, ethics, creativity, feedback quality, and equity received less attention. Assessment practices relied mainly on surveys, tests or rubrics, artifact analysis, and interviews, suggesting that much of the evidence base is shaped by short-term and readily implemented methods. Student and instructor perceptions were predominantly mixed, reflecting perceived benefits alongside concerns about pedagogy, integrity, workload, and governance. The review highlights the need for stronger alignment between learning constructs and assessment methods, more process-sensitive evidence, clearer reporting of governance practices, and greater attention to equity in GenAI-supported higher education.