Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2023 - Volume 8 Issue 3, Article No: 23761
https://doi.org/10.55267/iadt.07.13854

Published Online: 31 Aug 2023

Views: 392 | Downloads: 305

The integration of Artificial Intelligence (AI) technology with music instruction necessitates a delicate balance between technical advancement and the maintenance of humanistic teaching. This study examined how human-centered design concepts were used to optimize the integration of AI while also investigating the effects of AI technology on college-level music instruction in China. It aimed to identify potential, difficulties and make recommendations for ethical AI deployment in this particular environment. Semi-structured interviews with 20 music students and professors from Chinese higher education institutions were conducted using a qualitative study design. To condense significant themes and subthemes from the data, open coding, axial coding, and selective coding were used. The study revealed complex interactions between AI and Chinese music instruction. Themes included "Enhanced Learning with AI", emphasizing AI's role in motivating and personalizing music education; "User-Centric Design", emphasizing the importance of intuitive interfaces and aesthetic appeal; "Collaboration and Peer Learning", demonstrating AI's facilitation of collaborative projects; "Technical Challenges and Ethical Concerns", addressing technical obstacles and ethical concerns; and "Educator Support and Curriculum Alignment", emphasizing the importance of educator support and curriculum alignment. This study adds knowledge about how AI can be successfully incorporated into Chinese music teaching. It informs best practices for the adoption of AI, ensuring that technology enhances the learning experience for students while preserving cultural nuances. The study improves the conversation about innovative pedagogy and responsible technology integration. Implications include the potential for AI to change music education, cultural preservation, and global viewpoints. However, drawbacks such as sample bias and the dynamic nature of AI technology necessitate more study and development of educational techniques that use AI. Personalization and multimodal methods used in college music instruction in the future, to help increase student involvement. The importance of ethical issues, long-term effect analyses, and user-centered design will call for interdisciplinary cooperation. The future of AI-enhanced music education will also be shaped by assuring accessibility, diversity, and active engagement in policy and regulation discussions.

Artificial Intelligence Human-Centered Design Music Education Technology Integration

• Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 education. Computers and Education: Artificial Intelligence, 4, 100131. https://doi.org/10.1016/j.caeai.2023.100131
• Bhutoria, A. (2022). Personalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Computers and Education: Artificial Intelligence, 3, 100068. https://doi.org/10.1016/j.caeai.2022.100068
• Chen, H., Wen, Y., & Jin, J. (2023). Computer-aided teaching and learning of basic elementary functions. Heliyon, 9(5), e15987. https://doi.org/10.1016/j.heliyon.2023.e15987
• Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209, 118190. https://doi.org/10.1016/j.eswa.2022.118190
• Dai, C.-P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087
• Deb, D., & Fuad, M. (2021). Integrating big data and cloud computing topics into the computing curricula: A modular approach. Journal of Parallel and Distributed Computing, 157, 303-315. https://doi.org/10.1016/j.jpdc.2021.07.012
• Dias Pereira dos Santos, A., Loke, L., Yacef, K., & Martinez-Maldonado, R. (2022). Enriching teachers’ assessments of rhythmic Forró dance skills by modelling motion sensor data. International Journal of Human-Computer Studies, 161, 102776. https://doi.org/10.1016/j.ijhcs.2022.102776
• Elmosnino, S. (2021). Educational Strategies in Critical Listening: Interviews with Instructors. In Audio Engineering Society Conference: 2021 AES International Audio Education Conference. Audio Engineering Society. Retrieved from https://www.aes.org/e-lib/browse.cfm?elib=21196
• Ergano, M., Gerbi, A., Hamba, N., Getachew, B., Etefa, T., Nigatu, T. A., Debela, L., Bajiro, M., Kebede, W., & Tesfaye, S. (2020). Assessment of the determinants of Knowledge, Attitude and Practice (KAP) of Ethiopian Medical Students towards ethical Cadaver Dissection. Translational Research in Anatomy, 19, 100067. https://doi.org/10.1016/j.tria.2020.100067
• Fan, X., & Zhong, X. (2022). Artificial intelligence-based creative thinking skill analysis model using human–computer interaction in art design teaching. Computers and Electrical Engineering, 100, 107957. https://doi.org/10.1016/j.compeleceng.2022.107957
• Fondo, M., & Gómez-Rey, P. (2021, September). Integrating Game-Based Learning for Intercultural Skills Development in Higher Education. In ECGBL 2021 15th European Conference on Game-Based Learning (pp. 213-221). Academic Conferences Limited. https://doi.org/10.34190/GBL.21.084
• Gómez-Galán, J., Martínez-López, J. Á., Lázaro-Pérez, C., & García-Cabrero, J. C. (2021). Open Innovation during Web Surfing: Topics of Interest and Rejection by Latin American College Students. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 17. https://doi.org/10.3390/joitmc7010017
• Grenha Teixeira, J., Pinho, N. F. de, & Patrício, L. (2019). Bringing service design to the development of health information systems: The case of the Portuguese national electronic health record. International Journal of Medical Informatics, 132, 103942. https://doi.org/10.1016/j.ijmedinf.2019.08.002
• Hu, M., & Wang, J. (2021). Artificial intelligence in dance education: Dance for students with special educational needs. Technology in Society, 67, 101784. https://doi.org/10.1016/j.techsoc.2021.101784
• Kaliakatsos-Papakostas, M., Floros, A., & Vrahatis, M. N. (2020). Artificial intelligence methods for music generation: a review and future perspectives. In X.-S. Yang (Ed.), Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications (pp. 217-245). Academic Press. https://doi.org/10.1016/B978-0-12-819714-1.00024-5
• Makini, S. P., Oguntola, I., & Roy, D. (2020). Spelling their pictures: The role of visual scaffolds in an authoring app for young children’s literacy and creativity. 2020 Interaction Design and Children Conference, IDC 2020, 372-384. https://doi.org/10.1145/3392063.3394392
• Monlezun, D. J. (2023). AI+patient safety: adaptive, embedded, intelligent. In D. J. Monlezun (Ed.), The Thinking Healthcare System: Artificial Intelligence and Human Equity (pp. 159-182). Academic Press. https://doi.org/10.1016/B978-0-443-18906-7.00007-6
• Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management, 43, 1-14. https://doi.org/10.1016/J.IJINFOMGT.2018.05.005
• Pisoni, G., Díaz-Rodríguez, N., Gijlers, H., & Tonolli, L. (2021). Human-centered artificial intelligence for designing accessible cultural heritage. Applied Sciences, 11(2), 870. https://doi.org/10.3390/app11020870
• Shin, J., Park, H., Park, C., Hwang, J., & You, S. H. (2019). Effects of artificial intelligence (AI) based Integrated Robotic-assisted Gait, Music, and Light Brain Fitness Training (BRAIN-FIT) on electroencephalography (EEG) brain mapping of frontal alpha asymmetry (FA) and associated psychological behaviors in anxiety and depression. IBRO Reports, 6, S288. https://doi.org/10.1016/j.ibror.2019.07.892
• Singh, S., Singh, U. S., & Nermend, M. (2022). Decision analysis of e-learning in bridging digital divide for education dissemination. Procedia Computer Science, 207, 1970-1980. https://doi.org/10.1016/j.procs.2022.09.256
• Tubadji, A., Huang, H., & Webber, D. J. (2021). Cultural proximity bias in AI-acceptability: The importance of being human. Technological Forecasting and Social Change, 173, 121100. https://doi.org/10.1016/j.techfore.2021.121100
• Wang, Z., Wu, Y., González, V. A., Zou, Y., del Rey Castillo, E., Arashpour, M., & Cabrera-Guerrero, G. (2023). User-centric immersive virtual reality development framework for data visualization and decision-making in infrastructure remote inspections. Advanced Engineering Informatics, 57, 102078. https://doi.org/10.1016/j.aei.2023.102078
• Wei, J., Karuppiah, M., & Prathik, A. (2022). College music education and teaching based on AI techniques. Computers and Electrical Engineering, 100, 107851. https://doi.org/10.1016/j.compeleceng.2022.107851
• Wu, Y. C. J., Wu, T., & Li, Y. (2019). Impact of using classroom response systems on students’ entrepreneurship learning experience. Computers in Human Behavior, 92, 634-645. https://doi.org/10.1016/j.chb.2017.08.013
• Zhang, L., Yu, X., & Goh, C. C. M. (2022). Improving Performance and Metacognitive Awareness in Listening through Metacognitive Instruction for Chinese Language Learners. RELC Journal. https://doi.org/10.1177/00336882221129645
• Zhou, J. (2020). Design of AI-based self-learning platform for college English listening. In 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 544-547). IEEE. https://doi.org/10.1109/MLBDBI51377.2020.00114

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.