Understanding Chinese <scp>EFL</scp> Teachers' Actual Experiences With <scp>AI</scp> ‐Mediated Instruction Through the Lens of Expectancy Value Theory

ABSTRACT As artificial intelligence (AI) technologies become increasingly embedded in language education, it is essential to move beyond perception‐based research and examine teachers' actual experiences with AI integration. Drawing on Expectancy Value Theory (EVT), this qualitative study explores how Chinese EFL teachers have engaged with AI tools in second language (L2) instruction. Semi‐structured interviews were conducted with 30 Chinese EFL teachers from diverse professional backgrounds to capture their firsthand experiences. Thematic analysis of the interview data revealed five key areas in which teachers reported positive experiences: instructional planning, language skills instruction, materials and content development, assessment and personalised learning. Teachers described AI tools as enhancing autonomy, improving efficiency, reducing workload and supporting instructional goals across various teaching contexts. These findings align with core components of EVT, particularly teachers' expectations of success and the perceived value of AI‐mediated instruction. The study offers both theoretical contributions to motivational research in language education and practical implications for teacher training, educational policy and AI‐supported curriculum design.