A Corpus-Informed Framework For Adaptive Immersive Foreign Language Learning In Uzbekistan

Abstract

This article proposes an adaptive immersive language-learning framework for Uzbekistan through exploratory secondary analysis of three published school interaction episodes. It examines instructional support, participation, mediation and technical ambiguity, translating these observations into a teacher-supervised architecture. Its central contribution is an evidence-validation gate that distinguishes independent performance, supported completion, unsuccessful performance and missing evidence before adaptation. An A1–A2 museum scenario illustrates task design and CEFR alignment. Future evaluation would compare adaptive VR, non-adaptive VR and matched classroom activities. The analysis establishes design requirements; it reports no newly collected learner data or demonstrated learning effects.