Generative artificial intelligence scaffolding for statistical competence in graduate nursing education.

BACKGROUND: Graduate nursing students often experience significant anxiety related to statistics and have difficulty applying quantitative knowledge to clinical research practice. Although generative artificial intelligence may function as a learning scaffold, evidence supporting its structured integration into pedagogy remains limited. PURPOSE: To evaluate the impact of a structured generative artificial intelligence-supported self-directed learning intervention on statistical competence, learning capacity, and motivation among graduate nursing students. METHODS: This quasi-experimental study employed a single-group repeated-measures design and included 15 graduate nursing students enrolled in a master's program in Taiwan. The study was conducted from September 10, 2024, to January 15, 2025, with assessments performed at Weeks 1, 10, and 18. The 18-week intervention followed a five-stage scaffolding protocol: (1) instruction in fundamental statistical concepts with instructor-guided support and demonstrations using the Statistical Package for the Social Sciences, (2) independent drafting, (3) online peer collaboration, (4) generative artificial intelligence-assisted learning support, and (5) in-class metacognitive verification. Data were analyzed using descriptive statistics, generalized estimating equations, Pearson's correlation coefficients, and stepwise multiple regression analysis. RESULTS: Overall learning capacity improved significantly over time (post-test estimate = 1.26, p < .001), with a very large observed effect size (Hedges' g = 3.75). Meaning construction, self-regulation, and interpersonal learning also improved significantly by the post-test (p < .001). Learning motivation increased slightly but was not statistically significant. In the stepwise multiple regression analysis, satisfaction was the only variable significantly associated with final statistics report scores (R2 = 0.61; adjusted R2 = 0.58, p = .001). CONCLUSIONS: Participants showed improvements in learning capacity following participation in the structured generative artificial intelligence-supported protocol. Learner satisfaction may be associated with higher statistical competence. These findings support the use of generative artificial intelligence as a guided educational scaffold rather than an unregulated shortcut.