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Cultural immersion and AI-supported instruction: international students' Chinese as a foreign language enjoyment
1School of Foreign Languages, Jianghan University, Wuhan, China.
Introduction:
While Foreign Language Enjoyment (FLE) has been widely investigated in English learning, Chinese as a Foreign Language Enjoyment (CFLE) remains underexplored, particularly in AI-supported culturally immersive contexts. Guided by Self-Determination Theory (SDT), this study examines how an AI-supported immersive Chinese culture course contributes to learners' psychological needs and the experience of CFLE among international students in China.
Methods:
A sequential explanatory mixed-methods design was used. Eighty-two international students joined the study completing a 40-item questionnaire assessing teacher-, learner-, and environment-related CFLE. Reliability and validity analyses, descriptive statistics, and Pearson correlation analyses were performed. Eight participants completed semi-structured interviews, and the interview data were analyzed thematically.
Results:
Participants reported moderately high levels of CFLE (M = 3.59-3.71, SD = 0.748-0.887). Reliability and validity analyses indicated satisfactory internal consistency, with Cronbach's α = 0.951. Pearson correlations revealed strong positive relationships among teacher-, learner-, and environment-related enjoyment (r = 0.743-0.805, p < 0.01), suggesting that CFLE functions as an integrated affective system. Thematic analysis showed that enjoyment was exprienced through culturally responsive teaching, learner competence development, authentic cultural immersion, and AI-supported teacher-mediated learning, despite inadequately explained AI feedback occasionally leading to frustration or self-doubt.
Discussion:
Interpreted through SDT, CFLE appeared to surface in learner's reported accounts when cultural immersion and AI-supported instruction was perceived as jointly satisfying learners' needs for autonomy, competence, and relatedness. The findings advance CFLE research and suggest implications for the design of AI-supported immersive CFL learning environments.