DEVELOPING PRE-SERVICE FOREIGN LANGUAGE TEACHERS’ AI COMPETENCE ON TPACK-AI AND TAICS TECHNOLOGICAL SELF-EFFICIENCY APPROACHES

This article examines the urgent issue of developing pre-service foreign language teachers’ AI competence in the context of global digitalization. The aim of the study is to provide a theoretical justification and experimental validation of a model for developing AI competence, based on the integration of the Technological Pedagogical Content Knowledge – Artificial Intelligence (TPACK-AI) framework and the Teacher AI Competence Self-efficacy (TAICS) component, implemented through a system of specialized teaching methods.

The research methodology is based on a mixed-methods approach and includes testing and survey of 84 students, as well as linguistic content analysis of reflective reports using the Linguistic Inquiry and Word Count (LIWC) tool. The training process was organized over the course of an academic semester and involved learning using generative language models to create teaching materials and text AI simulators to model real-life pedagogical situations. The technical conditions for conducting the training included access to language models, specialized AI services for teachers, services for practicing and assessing foreign language oral speech, and AI text simulators.

The effectiveness of the proposed model has been experimentally proven. The conclusions of the article are consistent with the stated objective and demonstrate that the systematic integration of AI tools, supported by the TPACK-AI+TAICS model, enables pre-service foreign language teachers’ AI competence development as well as a qualitative transformation of their pedagogical thinking, thereby helping to bridge the technological divide in education.