Artificial Intelligence Applications and Self-Directed Learning in Curriculum and Teaching Methods Artificial Intelligence and Self- Learning

This study investigated undergraduate students’ use of artificial intelligence (AI) applications in self-directed learning and their self-directed learning skills and behaviors. It also examined differences in these variables according to gender, academic year, and cumulative grade point average (GPA), and explored the relationship between AI usage and self-directed learning skills. A descriptive quantitative design was employed, and data was collected from 70 students enrolled in a Curriculum and Teaching Methods course using a validated 20-item questionnaire. Descriptive statistics, a three-way ANOVA, Scheffé post-hoc tests, and Pearson’s correlation coefficient were used for data analysis. The results indicated high levels of AI application use (M = 4.07, SD = 0.52) and self-directed learning skills (M = 3.98, SD = 0.57). Pearson’s correlation revealed a strong positive association between AI application use and self-directed learning skills (r = 0.797, p < .001). No statistically significant gender differences were found in either variable. A statistically significant effect of academic year was observed for AI usage; however, post-hoc comparisons did not identify significant pairwise differences. For self-directed learning skills, significant differences were found for academic year and GPA, with post-hoc results indicating higher scores among students with an Excellent GPA compared to those with Very Good and Good GPAs. The findings suggest a strong association between AI application use and self-directed learning skills, while variations across demographic variables are limited and primarily related to academic achievement.