Frontiers in psychology · 2026
Teacher-reviewed generative AI action-analysis materials for university dance training: action understanding, body awareness, feedback uptake, and revised performance.
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Qi Tianhang
Аннотация
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.
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.