A Methodological Systematic Review of Mixed‐Methods Research on AI and Teacher Cognition and Practice in Language Education (2015–2025)

The rich insights afforded by integrating qualitative and quantitative methods in mixed‐methods research (MMR) have given it enormous cachet in teacher education. One expression of this interest is the growing scholarship on the intersection of teacher cognition and practice and artificial intelligence (AI) in language education. To provide a picture of the trajectory of this intersection, this methodological systematic review sought to analyze 36 empirical studies employing MMR published between 2015 and 2025, retrieved through a PRISMA‐informed procedure and a set of inclusion and exclusion criteria. Findings showed a sharp increase in the number of studies in 2023–2025, mostly conducted in East Asia and the Middle East. They also manifested that the majority of the studies were conducted on teacher cognition, mainly on in‐service teachers in university and school contexts, with scant research solely directed at teacher instructional practice. Findings about different aspects of mixed‐methods used in the studies revealed a preference for the explanatory quan‐qual sequencing, insufficient reporting of the sequencing of data analysis in many studies, and mostly separated rather than integrated presentation of the findings. This systematic review has implications for AI‐based teacher education, highlighting the importance of explicit report design typology, sequencing, priority of strands, and integration procedures in framing research on teacher cognition and practice regarding AI use.