Psychometric properties of an instrument to assess the level of knowledge about artificial intelligence in university professors

Abstract

Introduction: Knowledge about AI in university professors allows them to integrate these technological tools into their teaching strategies and improve the quality of learning. Objective: To determine the sustainable factorial structure of the relationship dimension of an instrument to evaluate the level of knowledge about artificial intelligence in university professors. Methods: A cross-sectional metric validation study was conducted. A sample of 83 university professors was selected. An instrument on artificial intelligence for university professors was applied, consisting of 15 questions divided into three sections. Psychometric analysis was carried out to evaluate its validity and reliability. Results: The results show that Part 1 has an alpha coefficient of 0.77, Part 2 has an alpha coefficient of 0.65, and Part 3 has an alpha coefficient of 0.83. The alpha coefficients for each subscale (0.77 for Part 1, 0.65 for Part 2, and 0.83 for Part 3) indicate that the instrument has good internal consistency and that the questions within each subscale are related to each other. The χ2/gl ratio of 2.1 indicates a good fit of the model, and the GFI, NFI, and CFI values are close to 1, indicating a good fit of the model. Conclusions: The results of the present study support the validity, reliability, and sustainable factorial structure of the instrument on artificial intelligence for university professors, making it an appropriate tool to evaluate the level of knowledge about AI in university professors.