Investigating the Long-Term Impact of Continuous and Transitioned Intelligent Tutoring Systems in Ear Training
Abstract
Background and Aim: Ear training is a critical component of music education, enabling musicians to develop auditory perception, pitch recognition, and musical comprehension. Traditional ear training methods often rely on instructor-led drills and passive listening exercises, which may not accommodate individual learning paces and provide immediate feedback. Intelligent Tutoring Systems (ITS) have emerged as a promising solution, offering personalized, adaptive learning experiences. However, the long-term impact of ITS on ear training remains underexplored. This study aims to evaluate the effectiveness of continuous ITS exposure and the impact of transitioning from Sight-Singing and Ear Training Master (SSETM) to Yin-Ke (YK) over two semesters.
Materials and Methods: A quasi-experimental design was employed, involving 85 undergraduate students (64.7% female and 35.3% male) at a university in China. Participants were divided into two groups: Group A used YK continuously for both semesters, while Group B transitioned from SSETM in the first semester to YK in the second semester. Performance was assessed through pre- and post-tests measuring four auditory skills: aural dictation, interval recognition, chord recognition, and melodic dictation. Data were analyzed using paired samples t-tests to evaluate within-group improvements and Repeated Measures ANOVA to examine the effects of time and the transition to YK on skill development.
Results: The results indicate that ITS significantly improved students’ auditory skills over the course of two semesters. Group A (continuous YK exposure) showed significant gains in aural dictation (p < .001), chord recognition (p = .018), and melodic dictation (p = .002). Group B (SSETM to YK transition) demonstrated significant improvements in aural dictation (p < .001), interval recognition (p = .002), and chord recognition (p < .001). A Repeated Measures ANOVA confirmed that time had a significant effect on aural dictation (p = .023, η² = 0.061), interval recognition (p = .006, η² = 0.09), and chord recognition (p = .012, η² = 0.074), while melodic dictation showed only marginal improvement (p = .079, η² = 0.037). A significant interaction effect for chord recognition (p = .004, η² = 0.098) suggests that Group B experienced greater improvement in this skill after transitioning to YK. Post hoc comparisons confirmed significant gains in aural dictation (p < .001), interval recognition (p = .037), and chord recognition (p < .001), while melodic dictation did not show a significant improvement (p = .786). This indicates that neither intervention method substantially enhanced this skill. Group B’s significant gains after adopting YK confirm its superiority over SSETM in facilitating ear training. These findings confirm the long-term effectiveness of ITS, particularly for aural dictation, interval recognition, and chord recognition, while suggesting that additional instructional strategies may be needed to improve melodic dictation.
Conclusion: This study provides empirical support for the sustained effectiveness of ITS interventions in ear training, demonstrating that both continuous and transitioned ITS interventions improve auditory skills. The findings confirm YK’s superiority over SSETM, reinforcing its role as a viable instructional tool. However, the limited progress in melodic dictation suggests the need for supplementary pedagogical strategies. Future research should expand the sample size and include multiple universities to enhance generalizability. Investigating ITS applications at different educational levels, such as high school and middle school, could assess broader applicability. Additionally, exploring teachers' perspectives and their integration of ITS into instruction would provide valuable insights into adoption challenges and instructional effectiveness.
Article Details
How to Cite
Section
Articles
Copyright & License
Copyright (c) 2025 International Journal of Sociologies and Anthropologies Science Reviews

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the International Journal of Sociologies and Anthropologies Science Reviews is retained by the author(s) under the under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Aïmeur, E., Onana, F. S. M., & Saleman, A. (2006). Sprits: Secure pedagogical resources in intelligent tutoring systems. International Conference on Intelligent Tutoring Systems,
Arapgirlioğlu, H., & Özaltunoğlu, Ö. (2012). Examination of the dictation skills in ear training education in terms of socio-cultural variables. Journal of Human Sciences, 9(2), 61-81.
Baker, D. J., Monzingo, E., & Shanahan, D. (2018). Modeling aural skills dictation. Proceedings of the 15th International Conference on Music Perception and Cognition–Graz, Austria,
Bauer, W. I. (2013). The acquisition of musical technological, pedagogical and content knowledge. Journal of Music Teacher Education, 22(2), 51-64.
Buonviri, N. O. (2019). Effects of silence, sound, and singing on melodic dictation accuracy. Journal of Research in Music Education, 66(4), 365-374.
Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. Proceedings of the SIGCHI conference on Human factors in computing systems,
Cornelius, N., & Brown, J. L. (2020). The interaction of repetition and difficulty for working memory in melodic dictation tasks. Research Studies in Music Education, 42(3), 368-382.
Della Ventura, M. (2023). Intelligent Tutoring System and Learning: Complexity and Resilience. Conference on Smart Learning Ecosystems and Regional Development,
Fischer, F., Hmelo-Silver, C. E., Goldman, S. R., & Reimann, P. (2018). International handbook of the learning sciences. Routledge.
Fletcher, C., Hulusic, V., & Amelidis, P. (2019). Virtual reality ear training system: a study on spatialised audio in interval recognition. 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games),
Goldberg S. B., Tucker R. P., Greene P. A., Simpson T. L., Kearney D. J., Davidson R. J. (2017). Is mindfulness research methodology improving over time? A systematic review. PLOS ONE, 12(10), Article e0187298. https://doi.org/10.1371/journal.pone.0187298
Gunawan, K., Liliasari, S., Kaniawati, I., & Setiawan, W. (2020). Exploring science teachers’ lesson plans by the implementation of intelligent tutoring systems in blended learning environments. Universal Journal of Educational Research, 8(10), 4776-4783.
Holland, S. (2013). Artificial Intelligence in Music Education: a critical review. Readings in Music and Artificial Intelligence, 239-274.
Horacek, L., & Lefkoff, G. (1989). Programmed Ear Training: Intervals and Melody and Rhythm. Harcourt Brace Jovanovich,
Hou, C. (2024). Artificial Intelligence Technology Drives Intelligent Transformation of Music Education. Applied Mathematics and Nonlinear Sciences, 9(1), 1-10.
Jiang, J. (2022). Using Pitch Feature Matching to Design a Music Tutoring System Based on Deep Learning. Computational Intelligence and Neuroscience, 2022.
Killam, R. N., Lorton, P. V., & Schubert, E. D. (1975). Interval recognition: Identification of harmonic and melodic intervals. Journal of Music Theory, 19(2), 212-234.
Kim, S., & Cozzarin, J. (2023). A New Technical Ear Training Game and Its Effect on Critical Listening Skills. Applied Sciences, 13(9), 5357.
Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary issues in technology and teacher education, 9(1), 60-70.
Kurvinen, E., Kaila, E., Laakso, M.-J., & Salakoski, T. (2020). Long term effects on technology enhanced learning: The use of weekly digital lessons in mathematics. Informatics in Education, 19(1), 51-75.
Lampropoulos, G. (2023). Augmented reality and artificial intelligence in education: Toward immersive intelligent tutoring systems. In Augmented reality and artificial intelligence: The fusion of advanced technologies (pp. 137-146). Springer.
Leon, M. (2024). Leveraging Generative AI for On-Demand Tutoring as a New Paradigm in Education. International Journal on Cybernetics & Informatics (IJCI), 14(14), 17.
Marc, M., Burnett, R., Skousen, C., & Akaaboune, O. (2015). Accounting education and reform: A focus on pedagogical intervention and its long-term effects. The Accounting Educators Journal, 25, 67-93.
Marmoah, S., Murwaningsih, T., Nurhasanah, F., Saddhono, K., Sutomo, A. D., & Legowo, B. (2024). An Integration of AI and Traditional Methodology in the Education Field in Order to: Transform the Trends. 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE),
Mavromatis, P., & Brown, M. (2008). An intelligent tutoring system for tonal counterpoint: From process to structure. Proceedings of the fourth Conference on Interdisciplinary Musicology (CIM08) Thessaloniki, Greece, 3-6 July 2008, http://web.auth.gr/cim08/
McFee, B., & Bello, J. P. (2017). Structured Training for Large-Vocabulary Chord Recognition. ISMIR,
McVicar, M., Ni, Y., Santos-Rodriguez, R., & De Bie, T. (2011). Using online chord databases to enhance chord recognition. Journal of New Music Research, 40(2), 139-152.
Merchán Sánchez-Jara, J. F., González Gutiérrez, S., Cruz Rodríguez, J., & Syroyid Syroyid, B. (2024). Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and Opportunities. Education Sciences, 14(11), 1171.
Merritt, J., & Castro, D. (2020). Comprehensive aural skills: A flexible approach to rhythm, melody, and harmony. Routledge.
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers college record, 108(6), 1017-1054.
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142-163.
Niess, M. L. (2016). Technological Pedagogical Content Knowledge (TPACK) Framework for K-12 Teacher Preparation: Emerging Research and Opportunities: Emerging Research and Opportunities.
Paney, A. S., & Buonviri, N. O. (2017). Developing melodic dictation pedagogy: A survey of college theory instructors. Update: Applications of Research in Music Education, 36(1), 51-58.
Phon-Amnuaisuk, S., & Chee, K.-S. (2005). Interactivities in music intelligent tutoring system. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05),
Renzoni, K. B. (2022). THE FIRST-YEAR MUSIC MAJOR (1st Edition ed.).
Rosas-Rodriguez, F. E., Sagastegui-Castillo, P. B., & Cieza-Mostacero, S. E. (2022). DoSiLa: An Intelligent Tutoring System for Learning Music Content. 2022 17th Iberian Conference on Information Systems and Technologies (CISTI),
Schüler, N. (2021). Modern approaches to teaching sight singing and ear training. Facta Universitatis, Series: Visual Arts and Music, 083-092.
Serdaroglu, E. (2018). Ear training made easy: Using IOS based applications to assist ear training in children. European Journal of Medicine and Natural Sciences, 2(1), 61-68.
Shakya, S. (2024). Tools For Ear Training Pedagogy. Retrieved from: https://urn.fi/URN:NBN:fi:amk-2024060722114
Ventura, M. D. (2022). A Self-adaptive Learning Music Composition Algorithm as Virtual Tutor. IFIP International Conference on Artificial Intelligence Applications and Innovations,
Voogt, J., Fisser, P., Pareja Roblin, N., Tondeur, J., & van Braak, J. (2013). Technological pedagogical content knowledge–a review of the literature. Journal of computer assisted learning, 29(2), 109-121.
Wash, E. (2019). Using technology to enhance instruction and learning in the music classroom.
Watanabe, A. (2024). Have Courage to Use your Own Mind, with or without AI: The Relevance of Kant's Enlightenment to Higher Education in the Age of Artificial Intelligence. Electronic Journal of e-Learning, 22(2), 46-58.
Xu, B. (2024). Design and Development of Music Intelligent Education System Based on Artificial Intelligence. 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE),
Zhang, H., & Talagala, N. (2023). Artificial intelligence assisted violin performance learning. J. Emerg. Investigators. https://doi.org/10.59720/22-264