The relationship between AI-TPACK and self-efficacy of pre-service English language teachers

The rapid development of technological tools in education has led to the emergence of frameworks such as the Technological Pedagogical Content Knowledge (TPACK) model, which examines teacher competencies linked to critical qualities regarding the use of technology. In today's educational landscape, proficiency in technological tools positively influences teacher self-efficacy. As such, AI-TPACK (Artificial Intelligence Technological Pedagogical Content Knowledge) has become a crucial competency for pre-service English language teachers upon the leading dominance of AI. The present study aims to investigate the relationship between AI-TPACK and self-efficacy perceptions, reveal to what extent AI-TPACK sub-dimensions predict self-efficacy, and explore the underlying factors of fourth-year pre-service English language teachers' perceptions of both frameworks from their own perspectives. The study applies sequential explanatory mixed-methods. The sample consisted of fourth-year pre-service English language teachers who had completed relevant teaching practice and technology-related courses. In quantitative part, correlational research design and convenience sampling was employed to recruit participants with the desired characteristics, and 303 pre-service English language teachers volunteered to participate in the study. Two scales, i.e., Teachers' Self-Efficacy Scale (Tschannen-Moran & Woolfolk Hoy, 2001) and AI-TPACK Scale (Ning et al., 2024) were used as data collection tools. Data were analyzed using descriptive statistics, independent T-tests, one-way ANOVA, and multiple regression analysis. In the qualitative part, which includes case study method, focus group discussions were held, and the data were collected from 13 participants. The qualitative data were analyzed using Creswell (2007)'s data analysis spiral. The findings revealed a low positive correlation between AI-TPACK and teacher self-efficacy. No significant differences were found in AI-TPACK or self-efficacy based on gender, GPA, or teaching experience. However, a significant difference was observed in GPA scores regarding three sub-dimensions of AI-TPACK: content knowledge, pedagogical knowledge and AI-technological pedagogical knowledge. In addition, findings obtained from qualitative data revealed that fourth-year pre-service English language teachers do not possess sufficient knowledge, experience, and self-efficacy related to the use of AI-based tools in foreign language teaching, and it is also necessary to provide domain specific training in this area. Thus, the study concludes that pre-service English language teachers' self-efficacy levels are influenced by their AI-TPACK competencies, highlighting the importance of integrating AI-related knowledge into teacher education programs.