From cultural bias to critical awareness: LLM-mediated voice dialogue and intercultural competence in Chinese language learners

Intercultural communicative competence (ICC) is a core objective of foreign language education, yet providing learners with sufficient meaningful intercultural encounters remains a persistent challenge. Large language models (LLMs) may support such encounters through AI-mediated cultural dialogue, yet no prior study has empirically examined whether such dialogues can foster ICC development. This study addresses the gap through a quasi-experimental pretest-posttest design integrating Byram's ICC model with Kolb's experiential learning cycle. Sixty-two Chinese-as-a-Foreign-Language (CFL) learners from 17 countries participated in a six-week intervention. The experimental group (n = 32) engaged in structured voice-based cultural dialogue tasks with a Chinese-developed LLM; the control group (n = 30) received equivalent hours of conventional cultural instruction. Analysis of covariance (ANCOVA) results showed that the experimental group significantly outperformed the control group on both the Intercultural Sensitivity Scale (ISS, d = 0.82) and the Cultural Intelligence Scale (CQS, d = 0.83). Dimension-level analysis revealed a non-uniform pattern: Metacognitive CQ yielded the largest effect (d = 1.08), whilst Cognitive CQ showed the smallest (d = 0.52) and did not survive Bonferroni correction. Learner perception data indicated high ratings for cultural learning effectiveness but lower ratings for communicative authenticity. The study proposes a "problem-as-resource" strategy that transforms LLM cultural biases into material for cultivating critical cultural awareness, offering empirical evidence and reusable task templates for integrating LLMs into intercultural language education.