Cultural studies have garnered significant attention in China for many decades, and the cultivation of intercultural competence within the academic sphere has been meticulously developed, particularly within the university context. Although intercultural competence is inherently multidisciplinary, its cultivation is predominantly integrated into the pedagogy of foreign language instruction. The discourse surrounding intercultural communication is mainly led by educators and scholars in the field of foreign language studies. The SFLEP Intercultural Competence Contest comprises three pivotal tasks: the development of intercultural case studies, scenario analysis, and the narration of Chinese stories. The rapid development of AI has opened new avenues in the field of education, particularly in language learning. The integration of AI with traditional pedagogies like drama, multimodal learning, and the production-oriented approach has been observed to enrich the learning experience and improve intercultural competence. A thorough examination of the 6th iteration of the contest provides the foundation for this exploration. The study not only highlights the potential of AI integrated approaches but also underscores their significant relevance in enhancing scaffolding techniques in foreign language education.

Professional translators may resist highly directive AI assistance when it threatens perceived autonomy, authorship, and control. This study introduces the Recursive Control and Alignment Protocol (ReCAP), a reactance-aware framework for interactive machine translation. ReCAP combines a reactance-potential model with a recursive loop of Intent Inference, Reactance-Guided Generation, and Critical Feedback Integration. Its Adversarial Reactance Minimization objective balances translation quality against estimated autonomy costs, and the task-specific Critical Automation Capability Index (CACI) provides an exploratory behavioral summary of critical engagement. In an evaluation with 156 professional translators across four English-based bidirectional language-pair settings, ReCAP achieved a mean COMET × 100 score of 36.8, 2.8 points above QE-guided MT (34.0), the strongest baseline, across five seed-matched runs per method–language-pair configuration. Mixed-effects estimates showed lower reported reactance for ReCAP than QE-guided MT (β = −0.55, 95% CI [−0.71, −0.39]) and higher exploratory CACI scores than CHORUS (β = 0.64, 95% CI [0.44, 0.84]). Post hoc CACI checks remained task-specific and exploratory. These findings support a run-level translation-quality advantage and task-specific human-outcome associations under the evaluated conditions.