Leveraging AI-enhanced interventions for targeted EFL teacher development: advancing professional growth, digital literacy, and rapport-building in higher education

Artificial intelligence (AI) is positioned to extend English as a Foreign Language (EFL) teacher education, yet evidence on theory-aligned, tool-specific supports remains limited. Guided by Reflective Practice Theory, Second Language Acquisition Theory, and Nonlinear Dynamic Language Learning Theory, this explanatory mixed-methods randomized study evaluated three AI-enhanced micro-interventions for university EFL teachers: an AI-powered Reflective Practice Platform, an Adaptive Digital Skills Tutor, and an Emotion and Communication Insight Analyzer. The final analyzed sample comprised 187 teachers (screened N = 212), with 100% post-randomization retention. Validated surveys assessed professional growth, digital literacy, and rapport-building; AI usage analytics and rater-coded interviews (n = 91) provided process evidence. A prespecified ethical governance layer—informed consent, privacy safeguards, bias monitoring, and feedback-algorithm transparency—framed implementation. All interventions produced statistically significant perceived gains, with reflective practice supports yielding the largest overall improvements. Rapport-building showed the strongest change (η² = .942, p < .001), plausibly attributable to real-time sentiment-trajectory visualizations, turn-taking alerts, and just-in-time empathic rephrasing prompts. Given the unusually large effect size and reliance on self-report—potentially reflecting cultural context and high baseline motivation despite multi-method triangulation—findings should be interpreted as short-term perceived gains pending corroboration through classroom observations and student-reported outcomes.