Thesis:

Title : Design and Validation of an Artificial Intelligence Literacy Enhancement Model for Lower Secondary School Teachers (Case Study: Jowain County)

The present study was conducted with the aim of designing and validating an Artificial Intelligence Literacy Promotion Model for First-Year High School Teachers in Jovin County. This research was applied in terms of its purpose and mixed in terms of its methodology (qualitative-quantitative). In the qualitative section, using the qualitative content analysis method with an inductive approach and employing semi-structured interviews with 30 expert teachers and administrators of the first-year high school in Jovin County who were selected through purposive sampling with maximum diversity, the dimensions, components, and indicators of the Artificial Intelligence Literacy Promotion Model were identified and extracted. Qualitative data analysis was conducted based on the Claise seven-step model. Qualitative findings showed that the aforementioned model includes 4 main factors (individual-psychological, educational-professional, organizational-school, and infrastructure-technological) and 16 sub-components that were the basis for designing a researcher-made questionnaire. In the quantitative section, a questionnaire developed with 48 items and a five-point Likert scale was distributed among 100 first-year high school teachers in Juyin County (from a population of 120) using a stratified random sampling method based on gender and school type. The content validity of the tool was confirmed using the Delphi technique in two rounds with the participation of 15 experts in the fields of educational sciences, educational technology, and artificial intelligence, and its reliability was also confirmed by calculating the Cronbach's alpha coefficient of the entire questionnaire and each of the subscales. Quantitative data were analyzed using structural equation modeling with the partial least squares (PLS) approach and Smart PLS software. The results showed that the average variance extracted (AVE) was higher than 0.50, indicating the appropriate reliability and convergent validity of the model. Divergent validity was also confirmed by the Fornell and Larker criterion. The overall fit index (GOF) of the model was calculated to be 0.91, indicating a very strong fit of the research model. Finally, the final model included four main factors, sub-components, indicators, and structural relationships between them. The findings of this study indicate that improving teachers' AI literacy requires a multidimensional and systematic approach in which individual (especially technology acceptance), educational (quality of in-service courses), organizational (school principal support), and infrastructure (high-speed Internet access) factors are considered simultaneously. This model can be used as an operational framework for planning teacher empowerment courses, educational policy-making, and investing in technological infrastructure in secondary schools.

Keywords: Keywords: AI literacy, teacher professional development, lower secondary school, promotion model, structural equation modeling.