Assessment of Artificial Intelligence Literacy and Conceptual Structure Analysis Among Students of Allameh Tabataba’i University
Keywords:
Artificial Intelligence Literacy, Higher education, Students, Confirmatory factor analysis, Structural equation modelingAbstract
Given the increasing role of artificial intelligence (AI) technologies in higher education, assessing students' competencies in understanding and applying AI is essential. Accordingly, the aim of the present study is to evaluate AI literacy and analyze its conceptual structure among students at Allameh Tabataba’i University, with a focus on three components: AI literacy, AI self-efficacy, and AI self-management. This study is applied in its objective and descriptive–analytical in nature. Data were collected through a survey method using a questionnaire. The data were analyzed on two levels: (1) descriptive analysis and one-sample t-test to assess the level of AI literacy; (2) confirmatory factor analysis and structural equation modeling to evaluate the conceptual structure of the questionnaire. The research instrument was the AI Literacy Scale developed by Karolus et al. (2023), which was distributed among a sample of 203 students at Allameh Tabataba’i University during the 2024–2025 academic year. Data analysis was conducted using SPSS 26 and PLS 3 software. The results of the one-sample t-test indicated that the mean scores of students in all three components were significantly higher than the theoretical average. The confirmatory factor analysis revealed that the three-dimensional structure of the instrument had a satisfactory fit. Moreover, Cronbach’s alpha coefficients and composite reliability indices indicated high reliability of the instrument. The students under study demonstrated a relatively favorable level of AI literacy. These findings can contribute to the design of AI-related courses and educational resources in universities, particularly in enhancing students’ self-efficacy and practical skills.
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