MOTIVATION TO TEACH AND AI ANXIETY AMONG TESOL STUDENT-TEACHERS WITH DEGREE-YEAR AND GENDER DIFFERENCES - Ilhan, Berk (LLT Journal: A Journal on Language and Language Teaching, 2025)
Integrating Artificial Intelligence (AI) in language teaching may induce anxiety among educators. This quantitative study investigates the relationship between AI anxiety and teaching motivation among TESOL student-teachers, an area that remains under-researched. The participants were 146 student-teachers in a TESOL program with different degree-years and genders. Two questionnaires examined the relationship between two dependent variables, AI anxiety and motivation to teach, and two independent variables, degree-year and gender differences. The investigation of AI anxiety encompassed four factors: anxiety due to learning, job replacement, AI configuration, and sociotechnical blindness, while the motivation to teach two factors: intrinsic and extrinsic motivation. The analysis was conducted using Kruskal-Wallis, Mann-Whitney U, and correlational analysis, and the findings revealed moderate levels of AI anxiety and motivation to teach. A significant difference in “job replacement anxiety” and the degree-year indicated that student-teachers could have different levels of job replacement anxiety in different years. Female student-teachers had higher AI anxiety and motivation to teach than male candidates. The minor positive correlations (%7-8) between AI anxiety, particularly the sociotechnical blindness factor, and intrinsic motivation show that intrinsic motivation could determine the anxiety level; therefore, teacher educators could give particular attention to reducing the sociotechnical blindness.