Fostering student motivation in AI-mediated EFL classrooms: evidence from China and Russia

Abstract

Integrating generative AI into university EFL classrooms can noticeably lower students’ intrinsic motivation to learn. In a qualitative study, six award-winning EFL instructors from China and Russia were asked how they reshape their teaching roles to sustain learner motivation in AI-supported environments. The data came from three sources: semi-structured interviews, stimulated recall sessions, and systematic thematic analysis of real classroom teaching materials. The analysis pointed to three interconnected pedagogical functions. Effective teachers strengthen learner autonomy by working with students to set joint guidelines for human-AI collaboration; they design culturally grounded, real-world tasks that call for meaningful language use with AI; and they build in metacognitive prompts that train students to critically assess what algorithms produce. The study offers a practical reflection checklist for everyday classroom use. The main argument is that human-centered teaching design remains necessary, it keeps technological efficiency from overtaking long-term motivational development. The findings have clear cross-cultural implications for university foreign language teaching.