Frontiers in psychology · 2026
The relationship between interdisciplinary learning motivation and learning burnout among university music majors in the context of AI-assisted music education: the mediating role of perceived learning load.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhang Ya
Аннотация
INTRODUCTION: University music majors increasingly encounter AI-related learning tasks while managing instrumental or vocal practice, ensemble rehearsals, theory coursework, performance assessment, and teaching preparation. This study examined the relationships among autonomous interdisciplinary motivation, pressure-driven interdisciplinary motivation, perceived learning load, and learning burnout in this combined learning context.
METHODS: Anonymous cross-sectional questionnaire data were collected from a convenience sample of 237 first- to third-year music majors at one university. The data were analyzed using reliability estimation, confirmatory factor analysis, Pearson correlation analysis, hierarchical regression, and Bootstrap testing with 5,000 resamples.
RESULTS: Autonomous interdisciplinary motivation was associated with lower learning burnout, whereas pressure-driven interdisciplinary motivation was associated with higher learning burnout. Perceived learning load accounted for part of the association between pressure-driven interdisciplinary motivation and learning burnout, indicating a significant cross-sectional indirect effect through students' appraisal of time, energy, task density, and competence demands. Autonomous and pressure-driven interdisciplinary motivation was nearly independent in the sample. The four-factor measurement model also provided preliminary support for treating autonomous motivation, pressure-driven motivation, perceived learning load, and learning burnout as distinct constructs.
DISCUSSION: The findings suggest that the psychological consequences of AI-related learning among university music majors depend not only on exposure to AI tools but also on students' reasons for participation and their appraisal of the resulting learning demands. Supporting autonomous engagement and managing additional learning load may help reduce burnout in AI-assisted music education.
METHODS: Anonymous cross-sectional questionnaire data were collected from a convenience sample of 237 first- to third-year music majors at one university. The data were analyzed using reliability estimation, confirmatory factor analysis, Pearson correlation analysis, hierarchical regression, and Bootstrap testing with 5,000 resamples.
RESULTS: Autonomous interdisciplinary motivation was associated with lower learning burnout, whereas pressure-driven interdisciplinary motivation was associated with higher learning burnout. Perceived learning load accounted for part of the association between pressure-driven interdisciplinary motivation and learning burnout, indicating a significant cross-sectional indirect effect through students' appraisal of time, energy, task density, and competence demands. Autonomous and pressure-driven interdisciplinary motivation was nearly independent in the sample. The four-factor measurement model also provided preliminary support for treating autonomous motivation, pressure-driven motivation, perceived learning load, and learning burnout as distinct constructs.
DISCUSSION: The findings suggest that the psychological consequences of AI-related learning among university music majors depend not only on exposure to AI tools but also on students' reasons for participation and their appraisal of the resulting learning demands. Supporting autonomous engagement and managing additional learning load may help reduce burnout in AI-assisted music education.