Teacher-reviewed generative AI action-analysis materials for university dance training: action understanding, body awareness, feedback uptake, and revised performance

Introduction University dance training requires beginners to translate demonstration, verbal correction, and bodily sensation into revised movement attempts. This study examined whether teacher-reviewed generative artificial intelligence (GenAI) action-analysis materials were associated with stronger action-unit learning than conventional teacher text cues in a university dance course. Methods A within-student crossover quasi-experimental design included 143 valid students and 827 valid action-unit records across six dance actions. Each student encountered both material conditions on different action units. The primary outcome was teacher-rated revised training quality after feedback, while process variables included action understanding, immediate body awareness, and feedback adoption. Two teachers independently scored each action-unit performance. Mixed-effects and repeated-measures ordinal models were used to account for repeated action-unit records. Results Teacher-reviewed GenAI action-analysis materials were associated with higher revised training quality than conventional teacher text cues (estimate = 5.09, 95% CI [4.14, 6.03]), higher action understanding (estimate = 1.97, 95% CI [1.62, 2.32]), and higher immediate body awareness (estimate = 0.36, 95% CI [0.28, 0.43]). A long-format phase-by-material sensitivity model also showed a larger initial-to-revised change under the GenAI condition (interaction = 1.68, 95% CI [1.01, 2.34]). In the process model, initial performance had the largest standardized coefficient, while action understanding, immediate body awareness, and feedback adoption remained positive predictors of revised training quality. Discussion Teacher-reviewed GenAI action-analysis materials were associated with stronger revised performance and more favorable learning-process indicators than conventional teacher text cues. The findings suggest that GenAI-supported action analysis may be useful when incorporated into teacher-mediated dance instruction, particularly as a structured aid for interpreting feedback and refining movement attempts.