Technology Acceptance, Actual Use, and Learning Outcomes in AI-Assisted Music Teaching: Evidence from Chinese University Students
Keywords:
Artificial intelligence-assisted music teaching, learning outcomes, Technology Acceptance Model, Constructivist Learning Theory, university students, music educationAbstract
The objectives of this research were (1) to examine the influence of facilitating conditions and perceived ease of use on perceived usefulness and students’ attitudes toward AI-assisted music teaching, (2) to investigate the influence of perceived usefulness and attitude on students’ behavioral intention and actual use of AI-assisted music teaching, and (3) to examine the influence of perceived usefulness and actual use of AI-assisted music teaching on students’ learning outcomes. This study employed a quantitative research approach, collecting data through an online questionnaire using purposive sampling combined with convenience and random sampling from 511 valid university students majoring in music from multiple universities across different regions of China who had experience with or exposure to AI-assisted music teaching tools. The analysis included descriptive statistics, measurement model evaluation, and structural model evaluation using partial least squares structural equation modeling to test the proposed hypotheses.Major findings: (1) on the influence of facilitating conditions and perceived ease of use, the results showed that facilitating conditions significantly influenced perceived ease of use and perceived usefulness, while perceived ease of use significantly influenced perceived usefulness and students’ attitudes toward AI-assisted music teaching; (2) on the influence of perceived usefulness and attitude, the results showed that perceived usefulness significantly influenced attitude and behavioral intention, while attitude significantly influenced behavioral intention and behavioral intention significantly influenced actual use of AI-assisted music teaching; and (3) on the influence of perceived usefulness and actual use on learning outcomes, the results showed that perceived usefulness and actual use both significantly influenced students’ learning outcomes.
References
Al-Hail, M., Zguir, M. F., & Koç, M. (2023). University students’ and educators’ perceptions on the use of digital and social media platforms: A sentiment analysis and a multi-country review. Science, 26(8), 107322. https://doi.org/10.1016/j.isci.2023.107322
Alam, M. N., Islam, M. A., Babiker, M. O. A., Siddiqui, M. S., Amin, M. B., & Oláh, J. (2026). AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective. Computers in Human Behavior Reports, 21, 100986. https://doi.org/10.1016/j.chbr.2026.100986
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: complex or simple? Research case examples. J Res Nurs, 25(8), 652-661. https://doi.org/10.1177/1744987120927206
Chai, C. S., Lin, P.-Y., Jong, M. S.-Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and Behavioral Intentions towards Learning Artificial Intelligence in Primary School Students. Educational Technology & Society, 24(3), 89-101. https://www.jstor.org/stable/27032858
Chen, B., Chang, Y., Wang, B., Zou, J., & Tu, S. (2024). Technology acceptance model perspective on the intention to participate in medical talents training in China. Heliyon, 10(4), e26206. https://doi.org/10.1016/j.heliyon.2024.e26206
Chen, X., Jiang, L., Zhou, Z., & Li, D. (2025). Impact of perceived ease of use and perceived usefulness of humanoid robots on students' intention to use. Acta Psychologica, 258, 105217. https://doi.org/10.1016/j.actpsy.2025.105217
Chen, Y., & Sun, Y. (2024). The Usage of Artificial Intelligence Technology in Music Education System Under Deep Learning. IEEE Access, PP, 1-1. https://doi.org/10.1109/ACCESS.2024.3459791
Cheng, L. (2025). The impact of generative AI on school music education: Challenges and recommendations. Arts Education Policy Review, 126. https://doi.org/10.1080/10632913.2025.2451373
Cherukunnath, D., & Singh, A. P. (2022). Exploring Cognitive Processes of Knowledge Acquisition to Upgrade Academic Practices. Front Psychol, 13, 682628. https://doi.org/10.3389/fpsyg.2022.682628
Cohen, J. (1988). Set Correlation and Contingency Tables. Applied Psychological Measurement, 12(4), 425-434. https://doi.org/10.1177/014662168801200410
Cooksey, R. W. (2020). Descriptive statistics for summarising data. In Illustrating statistical procedures: Finding meaning in quantitative data, 61-139.
Cullen, S., & Oppenheimer, D. (2024). Choosing to learn: The importance of student autonomy in higher education. Sci Adv, 10(29), eado6759. https://doi.org/10.1126/sciadv.ado6759
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). Technology acceptance model. Journal Management Science, 35(8), 982-1003.
Do, H. N., Do, B. N., & Nguyen, M. H. (2023). 3How do constructivism learning environments generate better motivation and learning strategies? The Design Science Approach. Heliyon, 9(12), e22862. https://doi.org/10.1016/j.heliyon.2023.e22862
Do, N., Do, B., & Nguyen, H. (2023). How do constructivism learning environments generate better motivation and learning strategies? The Design Science Approach. Heliyon, 9, e22862. https://doi.org/10.1016/j.heliyon.2023.e22862
Dong, L., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence, 8, 100400. https://doi.org/10.1016/j.caeai.2025.100400
Dou, G., & Feng, Y. (2025). Psychometric validation of the information technology acceptance scale for Chinese high school teacher. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1678302
Du, Y. (2025). Development of Artificial Intelligence-Assisted Interactive Platform for Music Education. International Journal of Information and Communication Technology Education, 21(1). https://doi.org/10.4018/IJICTE.392503
Duy, N., Phuong, T., Chau, V., Nhi, N., Khuyen, V., & Thi Phuong Giang, N. (2024). AI-assisted learning: an empirical study on student application behavior. Multidisciplinary Science Journal, 7, 2025275. https://doi.org/10.31893/multiscience.2025275
Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digit Health, 4(4), e0000764. https://doi.org/10.1371/journal.pdig.0000764
Gong, X., & Mao, M. (2026). Perceived Usefulness, Trust, and Behavioral Intention: A Study on College Student User Adoption Behaviors of Artificial Intelligence Generated News Based on Technology Acceptance Model. Big Data, 14(1), 56-61. https://doi.org/10.1177/2167647x261423109
Hair, J., Hult, G. T. M., Ringle, C., Sarstedt, M., Danks, N., & Ray, S. (2021). Evaluation of Reflective Measurement Models. In (pp. 75-90). https://doi.org/10.1007/978-3-030-80519-7_4
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
Hamid, M., Sami, W., & Sidik, M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series, 890, 012163. https://doi.org/10.1088/1742-6596/890/1/012163
Hu, S. (2024). The Effect of Artificial Intelligence-Assisted Personalized Learning on Student Learning Outcomes: A Meta-Analysis Based on 31 Empirical Research Papers. Science Insights Education Frontiers, 24, 3873-3894. https://doi.org/10.15354/sief.24.re395
Huang, M. (2024). Student engagement and speaking performance in AI-assisted learning environments: A mixed-methods study from Chinese middle schools. Education and Information Technologies, 30, 7143-7165. https://doi.org/10.1007/s10639-024-12989-1
Huang, X., & Lajoie, S. P. (2023). Social emotional interaction in collaborative learning: Why it matters and how can we measure it? Social Sciences & Humanities Open, 7(1), 100447. https://doi.org/10.1016/j.ssaho.2023.100447
Imran, M., Almusharraf, N., & Abbasova, M. (2024). Artificial Intelligence in Higher Education: Enhancing Learning Systems and Transforming Educational Paradigms. International Journal of Interactive Mobile Technologies (iJIM), 18, 34-48. https://doi.org/10.3991/ijim.v18i18.49143
Jiang, S., Li, H., & Gan, D. (2025). Technology acceptance model for online education: identifying interdisciplinary topics and their evolution based on BERTopic model. Social Sciences & Humanities Open, 12, 101831. https://doi.org/10.1016/j.ssaho.2025.101831
Jiang, W., Han, B., & Cui, Y. (2025). Influence of music educators on students' involvement in learning the theory of musical art. Acta psychologica, 253, 104722. https://doi.org/10.1016/j.actpsy.2025.104722
Joshi, D. R., Khanal, J., Chapai, K. P. S., & Adhikari, K. P. (2025). The impact of digital resource utilization on student learning outcomes and self-efficacy across different economic contexts: A comparative analysis of PISA, 2022. International Journal of Educational Research Open, 8, 100443. https://doi.org/10.1016/j.ijedro.2025.100443
Kim, J., & Moon, J. (2025). Determinants of Usefulness of Chat GPT for Learning in Technology Acceptance Model (TAM) Using Information Credibility, Fun, and Responsiveness and Moderating Role of Fun. SAGE Open, 15. https://doi.org/10.1177/21582440251320173
Lee, H., Kim, H., & Yan, W. (2025). Promoting inclusive AI and technology in K-12 education: A review of context, instructional strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 9, 100478. https://doi.org/10.1016/j.caeai.2025.100478
Liu, G., & Ma, C. (2023). Measuring EFL learners' use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching. https://doi.org/10.1080/17501229.2023.2240316
Liu, Y., Ma, S., & Chen, Y. (2024). The impacts of learning motivation, emotional engagement and psychological capital on academic performance in a blended learning university course. Front Psychol, 15, 1357936. https://doi.org/10.3389/fpsyg.2024.1357936
Ma, C. (2025). China’s Achievements in Digital Education in the Wake of Education Informatization 2.0 Action Plan. Science Insights Education Frontiers, 27, 4435-4451. https://doi.org/10.15354/sief.25.re488
Maamor, H., Achim, N. a., Ahmad, N., Roszaman, N., Anuar, N., Azwa, N., Rahman, S., & Hamjah, N. (2024). The Effect of Artificial Intelligence (AI) on Students' Learning. Information Management and Business Review, 16, 856-867. https://doi.org/10.22610/imbr.v16i3S(I)a.4178
Maričić, M., Anđić, B., Soeharto, S., Mumcu, F., Cvjetićanin, S., & Lavicza, Z. (2025). The exploration of continuous teaching intention in emerging-technology environments through perceived cognitive load, usability, and teacher’s attitudes. Education and Information Technologies, 30(7), 9341-9370. https://doi.org/10.1007/s10639-024-13141-9
Md Zin, Z., Abdullah, A., Hanafi, H., & Sahib, F. (2024). The Constructivist Learning Theory: Exploring Key Technological Advancements in Learning Management Systems. International Journal of Modern Education, 6, 585-597. https://doi.org/10.35631/IJMOE.623040
Memon, M., Ting, H., Cheah, J.-H., Ramayah, T., Chuah, F., & Cham, T.-H. (2020). Sample Size for Survey Research: Review and Recommendations. Journal of applied structural equation modeling, 4(2), 1-20. https://doi.org/10.47263/JASEM.4(2)01
Nurtanto, M., Nawanksari, S., Sutrisno, V. L. P., Syahrudin, H., Kholifah, N., Rohmantoro, D., Utami, I. S., Mutohhari, F., & Hamid, M. A. (2025). Determinants of behavioral intentions and their impact on student performance in the use of AI technology in higher education in Indonesia: A SEM-PLS analysis based on TPB, UTAUT, and TAM frameworks. Social Sciences & Humanities Open, 11, 101638. https://doi.org/10.1016/j.ssaho.2025.101638
Nuryakin, N., Rakotoarizaka, N., & Musa, H. (2023). The Effect of Perceived Usefulness and Perceived Easy to Use on Student Satisfaction The Mediating Role of Attitude to Use Online Learning. Asia Pacific Management and Business Application, 011, 323-336. https://doi.org/10.21776/ub.apmba.2023.011.03.5
Osman, Z. (2025). Attitude as a Catalyst: The Role of Perceived Ease of Use, Perceived Usefulness, and Self-Efficacy in Shaping Student Intentions to Use Artificial Intelligence in Higher Education. International Journal of Academic Research in Accounting Finance and Management Sciences, 15, 201-215. https://doi.org/10.6007/IJARAFMS/v15-i1/24459
Otto, D., Assenmacher, V., Bente, A., Gellner, C., Waage, M., Deckert, R., ... & Kuche, J. (2024). Student acceptance of AI-based feedback systems: An analysis based on the technology acceptance model (TAM). In INTED2024 proceedings, 3695-3701.https://doi.org/10.21125/inted.2024.0973
Ou, J., Nogueira, J., & Qin, C. (2025). Exploring the impact of AI-assisted practice applications on music learners' performance, self-efficacy, and self-regulated learning. Front Psychol, 16, 1675762. https://doi.org/10.3389/fpsyg.2025.1675762
Park, Y. S., Konge, L., & Artino, A. (2019). The Positivism Paradigm of Research. Academic medicine, 95, 1. https://doi.org/10.1097/ACM.0000000000003093
Peng, M. Y., & Yan, X. (2022). Exploring the Influence of Determinants on Behavior Intention to Use of Multiple Media Kiosks Through Technology Readiness and Acceptance Model. Frontiers Psychology, 13, 852394. https://doi.org/10.3389/fpsyg.2022.852394
Shan, X., Wang, Y., & Luo, L. (2025). The effect of digital music education on non-cognitive abilities: Empirical evidence from China. Acta psychologica, 260, 105564. https://doi.org/10.1016/j.actpsy.2025.105564
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025). Artificial intelligence-enabled adaptive learning platforms: A review. Computers and Education: Artificial Intelligence, 9, 100429. https://doi.org/10.1016/j.caeai.2025.100429
Tan, X., Cheng, G., & Ling, M. H. (2025). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355. https://doi.org/10.1016/j.caeai.2024.100355
Wang, Q., Zhao, G., & Zeng, J. (2024). The effects of facilitating conditions, digital competence, and technostress on higher education students’ digital informal learning: A moderated mediation examination. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9324
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
Wang, X., Xu, X., Zhang, Y., Hao, S., & Jie, W. (2024). Exploring the impact of artificial intelligence application in personalized learning environments: thematic analysis of undergraduates’ perceptions in China. Humanities and Social Sciences Communications, 11(1), 1644. https://doi.org/10.1057/s41599-024-04168-x
Wiyono, B. B., Imron, A., Sumarsono, R. B., Musa, K., & Binti Mohd. Saat, R. (2025). The effect of constructivist learning and project-based learning model on students’ entrepreneurial competence in higher education. Cogent Education, 12(1), 2557606. https://doi.org/10.1080/2331186X.2025.2557606
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample Size Requirements for Structural Equation Models: An Evaluation of Power, Bias, and Solution Propriety. Educ Psychol Meas, 76(6), 913-934. https://doi.org/10.1177/0013164413495237
Wu, M.-J., Zhao, K., & Fils-Aime, F. (2022). Response rates of online surveys in published research: A meta-analysis. Computers in Human Behavior Reports, 7, 100206. https://doi.org/10.1016/j.chbr.2022.100206
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory. Front Psychol, 13, 870777. https://doi.org/10.3389/fpsyg.2022.870777
Wu, X., & Qin, Y. (2025). On the relationship between music students' negative emotions, artificial intelligence readiness, and their engagement. Acta psychologica, 253, 104760. https://doi.org/10.1016/j.actpsy.2025.104760
Xia, M., & Guo, S. (2025). Understanding learners' perceptions of artificial intelligence-mediated Informal Digital Learning of English: A Q methodology approach. Acta psychologica, 261, 105980. https://doi.org/10.1016/j.actpsy.2025.105980
Xu, M. (2026). Selecting AI-enabled music learning technologies in higher education using AHP and TOPSIS. Scientific Reports. https://doi.org/10.1038/s41598-026-43769-1
Yan Yan, C., & Jafri, N. B. (2026). Factors influencing the intention to use generative artificial intelligence in educational systems: a meta-analysis. BMC Psychol, 14(1). https://doi.org/10.1186/s40359-026-03957-0
Yang, X., & Li, Y. (2025). Long-term intervention through online courses in music education: Impact on assessment, performance, creativity, and musical culture. Acta psychologica, 259, 105363. https://doi.org/10.1016/j.actpsy.2025.105363
Zhang, L. (2025). Compositional tools based on artificial intelligence for choral artistic education: Enhancing creative skills in choral arrangements. Thinking Skills and Creativity, 56, 101768. https://doi.org/10.1016/j.tsc.2025.101768
Zhang, X., Huang, Y., Chen, M., & Wang, F. (2024). Exploring the Factors Affecting College Students’ Intention to Use Online Teaching Platforms Through an Extended Technology Acceptance Model. International Journal of Web-Based Learning and Teaching Technologies, 19(1),348656. https://doi.org/10.4018/IJWLTT.348656
Zhao, Z., An, Q., & Liu, J. (2025). Exploring AI tool adoption in higher education: evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support. Frontiers Psychology, 16, 1619391. https://doi.org/10.3389/fpsyg.2025.1619391
Zhu, Y. (2026). A study of the effect of AI usage on students' self-efficacy and academic performance in business education: Moderating effect of teacher support [Registered report - stage I]. Acta Psychologica, 263, 106256. https://doi.org/10.1016/j.actpsy.2026.106256