Keywords
Intercultural communicative competence
Scaffolding
Interactional sociolinguistics
Online EFL tutorials
Published : 2026-08-13
Abstract
During the digital transformation of foreign language education and the increasing integration of artificial intelligence (AI) into pedagogical practices, there is a notable deficiency in understanding how AI translanguaging platforms interactively scaffold the development of intercultural communicative competence (ICC) in online English as a Foreign Language (EFL) context. This mixed-methods study combines a quantitative evaluation of ICC change with a detailed qualitative interactional sociolinguistic analysis. It aims to explore both the efficacy of AI translanguaging platforms in fostering college EFL learners’ ICC and the micro-processes through which potential cross-cultural misunderstandings are identified, managed, and resolved in AI-mediated teacher-student tutorial dialogues. Adopting a sequential explanatory mixed-methods design supplemented by a multiple case study approach, the study enrolled 64 non-English major undergraduates and 6 experienced instructors from an applied university in southeastern China. An 8-week AI-assisted online intercultural tutorial intervention was implemented, and quantitative data were collected using a validated ICC scale [1]. Qualitative data were analyzed through an integrated theoretical framework of Sociocultural Theory (SCT) and Interactional Sociolinguistics (IS). Quantitative findings reveal a positive effect of AI translanguaging platforms on learners’ overall development of ICC, with the largest gains in critical cultural awareness, intercultural awareness, and pragmatic flexibility. Qualitative analysis reveals a two-phase interactional scaffolding process and concludes that AI translanguaging platforms function as dynamic, interactionally embedded scaffolds. Their effectiveness mainly arises from their integration into responsive human dialogue, rather than from autonomous operation. These findings advance a process-oriented model of technology-mediated intercultural learning, provide empirical evidence for digital intercultural language pedagogy, and provide practical implications for EFL instructors, instructional designers, and platform developers seeking to optimize AI-assisted intercultural teaching.
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