Unlocking the Beat: How AI Tools Drive Music Students' Motivation, Engagement, Creativity and Learning Success
Abstract
This study explores the relationships among music students' artificial intelligence (AI) perceptions, motivation, engagement, creativity and learning success. Through a random sampling method, 521 Chinese music students participated in the research, which employed a range of questionnaires to assess AI perceptions, motivation, engagement, learning outcomes and creativity. The study utilised SPSS (version 27) and AMOS (version 24) for comprehensive statistical analysis. Findings reveal a significant relationship between students' perceptions of AI in music education and their motivation, engagement and learning success. Positive AI perceptions were found to enhance motivation by increasing interest in innovative learning tools and fostering engagement through interactive AI‐based learning environments. Moreover, these perceptions were predictive of higher motivation, engagement and learning success. The study suggests that AI can play a crucial role in enhancing educational outcomes by making learning more interactive, personalised and engaging, thus improving overall student performance and creativity in music education.
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Male students may hesitate to participate in traditional music due to fear of social marginalization.
“Conversely, male students may hesitate to participate due to fear of social marginalization [18,34].”
From Attitudes and Behaviors toward traditional music among Chinese students: the role of individual, family, and school factors (2026) · cites this paper as [34]
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Using AI-driven chatbots to foster Chinese EFL students’ academic engagement: An intervention study
Wang, Y., & Xue, L. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108353 · Cited by 328 · see citing works
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- Authors
- Chen, L.
- Affiliations
- Sias University (CN)
- Corresponding author
- Chen, Lixia
- ORCID
- Chen, Lixia
- Venue
- European Journal of Education
- Publisher
- Wiley
- Published
- 2024-11-05
- Volume
- 60
- Issue
- 1
- ISSN
- 0141-8211 (ISSN-L)
- 1465-3435
- OpenAlex
- W4404082280
- Cited by
- 67
- References
- 5
- Citations by year
- 2026: 48 · 2025: 19
- FWCI
- 17.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Arts and Humanities › Music › Diverse Music Education Insights
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in music education, music students, student motivation, student engagement, creativity, learning success, AI perception, interactive learning environment, personalized learning
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