Chinese-language instructor support, psychological resources, and perceived AI chatbot engagement in Mandarin learning motivation among international students in China

The Chinese language has unique tonal phonology and script characteristics that pose specific problems for international students learning Mandarin, and the ways in which human and AI support can complement each other to activate students’ learning motivation are not well understood. Building on control-value theory (CVT), this study constructs models of the effects of Chinese-language instructor support on Mandarin learning motivation and of the mediating role in the process, which are then verified and strengthened through chatbot immersion in Mandarin. Based on CVT, this study constructs models of the effect of Chinese-language instructor support on Mandarin learning motivation and the role of the mediator in this process, and then verifies and strengthens these models through chatbot immersion in Mandarin. Survey responses were obtained from 406 degree-seeking students in four regional university hubs (Xi’an, Wuhan, Nanjing, and Hangzhou) and analyzed using PLS-SEM. The measurement model revealed good convergent, discriminant, and reliability. Instructors’ support had a direct effect on motivation ( β = 0.263) and an indirect effect through learning buoyancy ( β = 0.043) and self-concept ( β = 0.108), both mediations being significant. Each indirect path had a positive moderating effect of chatbot immersion, which strengthened the paths. The full model accounted for 34% of the variance in learning motivation. Results not only extend CVT to a logographic SLA setting but also demonstrate the complementary nature of human scaffolding and AI practice, providing practical recommendations for teacher training, resilience training, and technology integration in Chinese universities.