Generative AI-supported learning, self-directed learning, and motivation in a graduate seminar: an exploratory single-group study

Introduction Generative artificial intelligence (AI) offers new forms of learning support, but assistance with a task should be distinguished from independent learning. This exploratory study examined questionnaire changes during a 16-week graduate Training and Development Seminar at a national university in Taiwan and considered students' written accounts of AI use. Methods The reported course sample comprised 10 master's and three doctoral students. Thirteen responses were available at each measurement occasion; eight participants could be matched by email address and formed the paired analysis sample. A 20-item SDLI-based self-directed learning (SDL) questionnaire and a 24-item ARCS course motivation questionnaire were administered in Chinese. Results Across all responses, SDL means were 80.62 (SD = 10.24) at pretest and 87.38 (SD = 7.12) at posttest; motivation means were 98.08 (SD = 11.84) and 106.54 (SD = 9.61), respectively. In the eight verified pairs, mean changes were 7.25 and 9.88 points, respectively. Two-sided exhaustive signed-rank p-values were 0.015625 and 0.03125, with seven nonzero differences for each outcome. Cronbach's alpha was 0.895/0.864 for SDL and 0.941/0.900 for motivation at pretest/posttest. Written reflections described perceived learning support, professional relevance, and concerns about dependence on AI. Discussion The small matched subset, self-report measures, and absence of a comparison group prevent attributing the changes specifically to AI or generalizing them to the full cohort. The findings raise questions about learner judgment, output verification, and assessment of unaided understanding.