An extended theory of planned behavior model of generative AI use intention for music composition among Chinese music students

Artificial intelligence (AI) tools are increasingly being used in music composition, reshaping how musical ideas are generated, modified, and developed. Music students are an important group because their willingness to incorporate AI into future composition remains insufficiently understood. To address this gap, the present study extends the Theory of Planned Behavior by incorporating task-technology fit, AI trust, and AI anxiety to examine Chinese music students’ behavioral intention (BI) to use generative AI (GenAI) for music composition. A total of 892 undergraduate music students with prior hands-on experience using GenAI tools for composition-related tasks were recruited from five provincial-level regions in China, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis. PLS-SEM showed that AI trust and attitudes had the strongest positive associations with BI, while AI anxiety was negatively associated with BI, and the model explained 56.2% of its variance. From a predictive perspective, ANN ranked AI trust highest in relative predictive importance for BI, followed by attitudes, task-technology fit, AI anxiety, perceived behavioral control, and subjective norms. Overall, AI trust and attitudes showed comparatively prominent relationships with BI, whereas perceived behavioral control and subjective norms showed more limited roles in this predominantly low-frequency user sample. These findings suggest that educators could support students in developing calibrated judgments of GenAI reliability, evaluating its suitability for specific composition tasks, and addressing AI-related anxiety while preserving authorship and creative agency.