The moderating effect of demeographic characteristics on the relationship between prospective mathematics teachercandidates artificial intelligent literacy and their attitudestoward artificial intelligent

In this study, the literacy levels of prospective mathematics teachers towards artificial intelligence, their attitudes towards artificial intelligence, the relationship between these two variables and the moderating effect of some demographic characteristics were examined. In line with the aim of the study, the relational survey model, one of the quantitative research methods, was adopted as the research design. The population of the study consisted of prospective teachers studying in the mathematics teaching programs of state universities in the Central Anatolia Region. The sample of the study consisted of 832 teacher candidates studying in the mathematics teaching departments of the education faculties of six state universities (Aksaray University, Konya Necmettin Erbakan University, Karamanoğlu Mehmetbey University, Nevşehir Hacı Bektaş Veli University, Kırşehir Ahi Evran University and Niğde University) in the 2024-2025 academic year. One of the data collection tools used for the purpose of the research is the Artificial Intelligence Literacy Scale, developed by Wang et al. (2023) and adapted to Turkish by Polatgil and Güler (2023), and the other is the Artificial Intelligence Attitude Scale, developed by Schepman and Rodway (2020) and adapted to Turkish by Kaya et al. (2024). SPSS Process Macro was used for statistical analysis of the data. According to the results of the study; prospective mathematics teachers' literacy levels towards artificial intelligence are close to good level, their attitudes towards artificial intelligence are in the category of undecided, and a significant positive relationship was found between prospective mathematics teachers' literacy scores towards artificial intelligence and their attitude scores towards artificial intelligence. In addition, it was investigated whether gender, level of interest in AI applications, taking courses on artificial intelligence, duration of using technological applications in a day and frequency of using artificial intelligence academically had a moderating effect on this relationship and it was found that interest and duration of daily technological application use had a moderating effect.