University teachers’ perceptions, attitudes, and practices with generative AI: a systematic literature review with a meta-analytic component

Abstract This study presents a systematic literature review and quantitative synthesis of empirical research on university teachers’ engagement with generative artificial intelligence (GenAI) in higher education. Following PRISMA guidelines, 207 records were screened, resulting in 30 studies included in the qualitative synthesis and 13 studies contributing to the comparative quantitative synthesis using meta-analytic techniques. Several domains of GenAI use were identified through qualitative synthesis, revealing a diversity of attitudes that range from pedagogical benefits to concerns about ethics and integrity. Teachers mainly use GenAI for lesson preparation, feedback, and formative assessment, while traditional approaches are used for assessment of key student outputs. Despite the lack of general guidelines, the practices used by teachers were shaped based on institutional policies, professional development, and other background circumstances. Quantitative analyses complemented these insights. Pooled survey data indicated moderate acceptance levels. A comparative quantitative synthesis using meta-analytic techniques to integrate results from primary studies showed small but positive effects of individual and contextual factors, while subgroup comparisons demonstrated systematic variation across generations, disciplines, and institution types. Overall, the findings suggest that effective and responsible integration of GenAI requires not only strengthening teachers’ competencies but also supportive institutional and cultural environments.