Investigating the level of artificial intelligence literacy of university students using decision trees

Artificial intelligence (AI) literacy is critical to shaping students' academic experiences and future opportunities inhigher education. This study examines AI literacy among university students, examining variables such as gender, frequency of use of AI applications, completion of AI-related courses, and field of study. The research involved 664 students from a public university, using a survey model and data collected via the Artificial Intelligence Literacy Scale. Descriptive and decision tree analyses, specifically the CHAID algorithm, were used to determine the factors influencing AI literacy levels. The results showed that the students' overall AI literacy, critical aprasial and practical application were average and technical understanding was low. While the frequency of individuals' use of AI technology has a determining effect on the overall AI literacy scale and critical appraisal(CA) and practical applications(PA) dimensions, gender came to the fore in the technical understanding(TU) dimension. The overall scale, PA and CA, while the status of having taken a course in AI is effective for students who frequently use artificial intelligence tools, the type of faculty of study is effective for those who use it less frequently. In TU, the faculty of study for females and the frequency of using artificial intelligence tools for males are determinative. While the TU scores of male students are higher than those of female students, the AI literacy of students studying in the faculties of science and literature, engineering, technology and medicine are higher than those of students studying in the faculties of education, sports sciences and postgraduate students.