Recent Advances in Artificial Intelligence for Music Education

Abstract

In this survey, we present a focused analysis of recent advances in Artificial Intelligence (AI) for music education, structured around four key pedagogical domains: learning and practicing, assessment, creation, and teaching. First, for learning and practicing, we examine AI-driven tools for personalized skill acquisition, including intelligent instruments and adaptive systems that provide real-time feedback. Second, for assessment, we review progress in the automated assessment of musical performance, moving beyond pitch and rhythm to more nuanced expressive qualities. Third, for creation, we investigate innovations in AI-augmented composition, where generative models act as collaborative partners and creative catalysts for students. Finally, for teaching, we explore systems for teacher empowerment, such as automated resource generation and learning analytics dashboards. The analysis highlights the transformative role of Deep Learning (DL) and Generative AI (GAI) in each domain while critically discussing persistent technical limitations and emerging ethical concerns. By synthesizing interdisciplinary developments, this survey aims to chart the current frontier and inform future research at the intersection of AI technology and music pedagogy.

Keywords

music education / Artificial Intelligence / human-AI collaboration

Author Contributions

G.Y.: Conceptualization, investigation, writing, and revision; G.Z.: Conceptualization, investigation, writing, and revision; Z.Y.: Conceptualization, investigation, writing, and revision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Use of AI and AI-Assisted Technologies

During the preparation of this work, the authors used ChatGPT to check the grammar. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

References

[1] Achiam, J.; Adler, S.; Agarwal, S.; et al. GPT—4 Technical Report. arXiv 2023 , arXiv:2303.08774.

[2] Touvron, H.; Martin, L.; Stone, K.; et al. Llama 2: Open Foundation and Fine—Tuned Chat Models. arXiv 2023 , arXiv:2307.09288.

[3] Liu, A.; Feng, B.; Xue, B.; et al. DeepSeek—V3 Technical Report. arXiv 2024 , arXiv:2412.19437.

[4] Alpaydin, E. Introduction to Machine Learning , 4th ed.; MIT Press: Cambridge, MA, USA, 2020.

[5] Goodfellow, I.; Bengio, Y.; Courville, A.; et al. Deep Learning ; MIT Press: Cambridge, MA, USA, 2016.

[6] Goodfellow, I.J.; Pouget—Abadie, J.; Mirza, M.; et al. Generative Adversarial Nets. In Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada, 8—13 December 2014; Volume 27, pp. 2672-2680.

[7] Feuerriegel, S.; Hartmann, J.; Janiesch, C.; et al. Generative AI. Bus. Inf. Syst. Eng. 2024 , 66 , 111-126.

[8] Li, P.P.; Wang, B. Artificial Intelligence in Music Education. Int. J. Hum.—Comput. Interact. 2024 , 40 , 4183-4192.

[9] Zhang, Y.; Fen, B.W.; Zhang, C.; et al. Transforming Music Education Through Artificial Intelligence: A Systematic Literature Review on Enhancing Music Teaching and Learning. Int. J. Interact. Mob. Technol. 2024 , 18 , 76.

[10] Zhang, H. Artificial Intelligence in Music Education: A Scoping Review of Practices, Strategies, and Challenges. J. Adv. Res. Educ. 2025 , 4 , 69-82.

[11] Ruan, S. AI Harmonica: A Smart Electronic Harmonica for Music Learning and Co—Creativity. In Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA, USA, 2—7 December 2025.

[12] Blanchard, L.; Naseck, P.; Egozy, E.; et al. Developing Symbiotic Virtuosity: AI—Augmented Musical Instruments and Their Use in Live Music Performances. In An MIT Exploration of Generative AI ; MIT Press: Cambridge, MA, USA, 2024.

[13] Shu, X.; Shi, L.; Cheng, J.; et al. FretMate: ChatGPT—Powered Adaptive Guitar Learning Assistant. In Proceedings of the 30th International Conference on Intelligent User Interfaces, Cagliari, Sardinia, Italy, 24 —27 March 2025; pp. 715-726.

[14] Blanco, A.D.; Tassani, S.; Ramirez, R. Real—Time Sound and Motion Feedback for Violin Bow Technique Learning: A Controlled, Randomized Trial. Front. Psychol. 2021 , 12 , 648479.

[15] Cheng, D.; Qu, X. Intelligent Generation and Optimization of Resources in Music Teaching Reform Based on Artificial Intelligence and Deep Learning. Sci. Rep. 2025 , 15 , 30051.

[16] Holster, J. Augmenting Music Education Through AI: Practical Applications of ChatGPT. Music Educ. J. 2024 , 110 , 36-42.

[17] Possaghi, I.; Vesin, B.; Zhang, F.; et al. Integrating Multi—Modal Learning Analytics Dashboard in K—12 Education: Insights for Enhancing Orchestration and Teacher Decision—Making. Smart Learn. Environ. 2025 , 12 , 53.

[18] Cabral, L.; Pinto, R.; Goncalves, G. AI—Powered Learning Analytics Dashboards: A Systematic Review of Applications, Techniques, and Research Gaps. Discov. Educ. 2025 , 4 , 525.

[19] Yin, S.; Fu, C.; Zhao, S.; et al. A Survey on Multimodal Large Language Models. Natl. Sci. Rev. 2024 , 11 , nwae403.

[20] Downie, J.S. Music Information Retrieval. Annu. Rev. Inf. Sci. Technol. 2003 , 37 , 295-340.

[21] Benetos, E.; Dixon, S.; Duan, Z.; et al. Automatic Music Transcription: An Overview. IEEE Signal Process. Mag. 2018 , 36 , 20-30.

[22] Berenzweig, A.; Logan, B.; Ellis, D.P.; et al. A Large—Scale Evaluation of Acoustic and Subjective Music—Similarity Measures. Comput. Music J. 2004 , 28 , 63-76.

[23] Pons, J.; Serra, X. Musicnn: Pre—Trained Convolutional Neural Networks for Music Audio Tagging. In Proceedings of the 20th International Society for Music Information Retrieval Conference (ISMIR), Delft, The Netherlands, 4—8 November 2019.

[24] Kim, J.W.; Salamon, J.; Li, P.; et al. CREPE: A Convolutional Representation for Pitch Estimation. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 15—20 April 2018; pp. 161-165.

[25] Park, S.H.; Park, Y.H. Audio—Visual Tensor Fusion Network for Piano Player Posture Classification. Appl. Sci. 2020 , 10 , 6857.

[26] Zeng, M.; Tan, X.; Wang, R.; et al. MusicBERT: Symbolic Music Understanding with Large—Scale Pre—Training. In Proceedings of the Findings of the Association for Computational Linguistics: ACL—IJCNLP 2021, Association for Computational Linguistics, Online, 1—6 August 2021; pp. 791-800.

[27] Chou, Y.H.; Chen, I.C.; Ching, J.; et al. MidiBERT—Piano: Large—Scale Pre—Training for Symbolic Music Classification Tasks. J. Creat. Music Syst. 2024 , 8 . https://doi.org/10.5920/jcms.1064

[28] Yizhi, L.; Yuan, R.; Zhang, G.; et al. MERT: Acoustic Music Understanding Model with Large—Scale Self—Supervised Training. In Proceedings of the Twelfth International Conference on Learning Representations, Vienna, Austria, 7—11 May 2024.

[29] Zhang, H.; Liang, J.; Dixon, S. From Audio Encoders to Piano Judges: Benchmarking Performance Understanding for Solo Piano. In Proceedings of the 25th International Society for Music Information Retrieval Conference, San Francisco, CA, USA, 10—14 November 2024.

[30] Dhariwal, P.; Jun, H.; Payne, C.; et al. Jukebox: A Generative Model for Music. arXiv 2020 , arXiv:2005.00341.

[31] Agostinelli, A.; Denk, T.I.; Borsos, Z.; et al. MusicLM: Generating Music from Text. arXiv 2023 , arXiv:2301.11325.

[32] Copet, J.; Kreuk, F.; Gat, I.; et al. Simple and Controllable Music Generation. Adv. Neural Inf. Process. Syst. 2023 , 36 , 47704-47720.

[33] Li, K. Policy Gradient Reinforcement Learning for Personalized Pathways in Intelligent Music Education Systems. In Proceedings of the 2025 2nd International Conference on Informatics Education and Computer Technology Applications, Kuala Lumpur, Malaysia, 17—19 January 2025; pp. 128-132.

[34] Yahya, K.; Fard, P.R. Computational Model of Music Sight Reading: A Reinforcement Learning Approach. arXiv 2010 , arXiv:1007.0546.

[35] Cancino—Chacón, C.; Peter, S.; Hu, P.; et al. The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist. In Proceedings of the Thirty—Second International Joint Conference on Artificial Intelligence (IJCAI—23), Macao, China, 19—25 August 2023; pp. 5779-5787.

[36] Sedrakyan, G.; Malmberg, J.; Verbert, K.; et al. Linking Learning Behavior Analytics and Learning Science Concepts: Designing a Learning Analytics Dashboard for Feedback to Support Learning Regulation. Comput. Hum. Behav. 2020 , 107 , 105512.

[37] Schedl, M.; Gomez, E.; Urbano, J.; et al. Music Information Retrieval: Recent Developments and Applications. Found. Trends Inf. Retr. 2014 , 8 , 127-261.

[38] Morreale, F.; Martinez—Ramirez, M.A.; Masu, R.; et al. Reductive, Exclusionary, Normalising: The Limits of Generative AI Music. Trans. Int. Soc. Music Inf. Retr. 2025 , 8 , 300-312.

[39] Arcos, J.L. Music and Similarity Based Reasoning. In Soft Computing in Humanities and Social Sciences ; Springer: Berlin, Heidelberg, 2011; pp. 467-478.

[40] Rothstein, J. MIDI: A Comprehensive Introduction , 2nd ed.; A—R Editions, Inc.: Middleton, WI, USA, 1995; Volume 7.

[41] LeCun, Y.; Bottou, L.; Bengio, Y.; et al. Gradient—Based Learning Applied to Document Recognition. Proc. IEEE 2002 , 86 , 2278-2324.

[42] Hochreiter, S.; Schmidhuber, J. Long Short—Term Memory. Neural Comput. 1997 , 9 , 1735-1780.

[43] Roberts, A.; Engel, J.; Raffel, C.; et al. A Hierarchical Latent Vector Model for Learning Long—Term Structure in Music. In Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 10—15 July 2018; pp. 4364-4373.

[44] Vaswani, A.; Shazeer, N.; Parmar, N.; et al. Attention Is All You Need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4—9 December 2017, Volume 30.

[45] Sohl—Dickstein, J.; Weiss, E.; Maheswaranathan, N.; et al. Deep Unsupervised Learning Using Nonequilibrium Thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 6—11 July 2015; pp. 2256-2265.

[46] Ho, J.; Jain, A.; Abbeel, P. Denoising Diffusion Probabilistic Models. Adv. Neural Inf. Process. Syst. 2020 , 33 , 6840-6851.

[47] Chen, J.; Xue, W.; Tan, X.; et al. FastSAG: Towards Fast Non—Autoregressive Singing Accompaniment Generation. In Proceedings of the Thirty—Third International Joint Conference on Artificial Intelligence, Jeju, South Korea, 3—9 August 2024; pp. 7618-7626.

[48] Bengio, Y.; Ducharme, R.; Vincent, P.; et al. A Neural Probabilistic Language Model. J. Mach. Learn. Res. 2003 , 3 , 1137-1155.

[49] Payne, C. MuseNet. Available online: https://openai.com/blog/musenet (accessed on 12 January 2026).

[50] Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst. 2012 , 25 .

[51] Yoo, S.J.; Shrestha, S.; Muresanu, I.; et al. VioPose: Violin Performance 4D Pose Estimation by Hierarchical Audiovisual Inference. In Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, USA, 26 February—6 March 2025; pp. 1-12.

[52] Provenzale, C.; Di Tommaso, F.; Di Stefano, N.; et al. Real—Time Visual Feedback Based on MIMUs Technology Reduces Bowing Errors in Beginner Violin Students. Sensors 2024 , 24 , 3961.

[53] Mao, Z.; Zhao, M.; Wu, Q.; et al. DeepResonance: Enhancing Multimodal Music Understanding via Music—Centric Multi—Way Instruction Tuning. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Suzhou, China, 4—9 November 2025; pp. 12926-12948.

[54] Ramesh, A.; Pavlov, M.; Goh, G.; et al. Zero—Shot Text—to—Image Generation. In Proceedings of the 38th International Conference on Machine Learning, Online, 18—24 July 2021 ; pp. 8821-8831.

[55] Moringen, A.; Rüttgers, S.; Zintgraf, L.; et al. Optimizing Piano Practice with a Utility—Based Scaffold. arXiv 2021 , arXiv:2106.12937.

[56] Phanichraksaphong, V.; Tsai, W.H. Automatic Evaluation of Piano Performances for STEAM Education. Appl. Sci. 2021 , 11 , 11783.

[57] Pati, K.A.; Gururani, S.; Lerch, A. Assessment of Student Music Performances Using Deep Neural Networks. Appl. Sci. 2018 , 8 , 507.

[58] Wang, X.; Fitri bin Haris, M. AI—Driven Violin Performance Evaluation: A Deep Learning Approach for Automated Assessment and Personalized Feedback in Music Education. In Proceedings of the 2025 International Conference on Generative Artificial Intelligence and Digital Media, Dali, China, 14—16 March 2025; pp. 126-132.

[59] Zhang, H.; Cheung, V.K.; Nishioka, H.; et al. LLaQo: Towards a Query—Based Coach in Expressive Music Performance Assessment. In Proceedings of the 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 6—11 April 2025; pp. 1-5.

[60] Jin, C.; Wang, T.; Liu, S.; et al. A Transformer—Based Model for Multi—Track Music Generation. Int. J. Multimed. Data Eng. Manag. 2020 , 11 , 36-54.

[61] Pan, Z.; Biegley, L.; Taylor, A.; et al. A Systematic Review of Learning Analytics: Incorporated Instructional Interventions on Learning Management Systems. J. Learn. Anal. 2024 , 11 , 52-72.

[62] Chang, J.; Wang, Z.; Yan, C. MusicARLtrans Net: A Multimodal Agent Interactive Music Education System Driven via Reinforcement Learning. Front. Neurorobot. 2024 , 18 , 1479694.

[63] Xiao, H. Personalized Learning Path Planning and Effect Verification of Music Education Information System Empowered by Artificial Intelligence. In Proceedings of the 2025 International Conference on Artificial Intelligence and Educational Systems, Beijing, China, 25—27 April 2025; pp. 443-448.

[64] Rom, B.A. Scaffolding Autonomy in the Practice Room: A Mixed Methods Study Examining the Impact of Digital Scaffolds on High School String Musicians’ Self—Correction and Improvement of Pitch and Rhythmic Accuracy During Independent Music Practicing. Ph.D. Thesis, The University of Nebraska—Lincoln, Lincoln, NE, USA, 2020.

[65] Mateos—Moreno, D.; Garc’ıa—Perals, J.; Maxwell, T. Teaching to Practice Productively and Consciously: An Action—Research Study in One—to—One Instrumental Music Teaching. Music Educ. Res. 2025 , 27 , 203-214.

[66] Butler, A.; Lind, V.L.; McKoy, C.L. Equity and Access in Music Education: Conceptualizing Culture as Barriers to and Supports for Music Learning. Music Educ. Res. 2007 , 9 , 241-253.

[67] Webster, P.R. Computer—Based Technology and Music Teaching and Learning. In Critical Essays in Music Education ; Routledge: London, UK, 2017; pp. 321-344.

[68] Acquilino, A. Real—Time Computational Feedback Systems for Enriched Music Pedagogy. Ph.D. Thesis, McGill University, Montreal, QC, Canada, 2025.

[69] Lu, L. AI—Powered Intelligent Music Education Systems for Real—Time Feedback and Performance Assessment. Int. J. Inf. Commun. Technol. 2025 , 26 , 33-47.

[70] Chellamani, G.K.; N, A.; C, C.; et al. SpectroFusionNet: A CNN Approach Utilizing Spectrogram Fusion for Electric Guitar Play Recognition. Sci. Rep. 2025 , 15 , 16842.

[71] Cao, Z.; Hidalgo, G.; Simon, T.; et al. OpenPose: Realtime Multi—Person 2D Pose Estimation Using Part Affinity Fields. IEEE Trans. Pattern Anal. Mach. Intell. 2019 , 43 , 172-186.

[72] Bazarevsky, V.; Grishchenko, I.; Raveendran, K.; et al. BlazePose: On—Device Real—Time Body Pose Tracking. arXiv 2020 , arXiv:2006.10204.

[73] Mizuho, Y.; Sugiura, Y. A Comparison of Violin Bowing Pressure and Position among Expert Players and Beginners. In Proceedings of the AsiaHaptics 2024, Selangor, Malaysia, 28—30 October 2024.

[74] Almeida, A.; Li, W.; Schubert, E.; et al. Recording and Analysing Physical Control Variables Used in Clarinet Playing: A Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT). Front. Signal Process. 2023 , 3 , 1089366.

[75] Grosshauser, T.; Hermann, T. Sensor Fusion and Multi—Modal Feedback for Musical Instrument Learning and Teaching. In Proceedings of the Second Vienna Talk Music Acoust, Vienna, Austria, 19—21 September 2010.

[76] Matsuoka, Y.; Sako, S. Attribute—Aware Deep Music Transformation for Polyphonic Music. In Proceedings of the Late Breaking Demo in the 22nd International Society for Music Information Retrieval Conference (ISMIR), Online, 7—12 November 2021.

[77] Kawai, L.; Esling, P.; Harada, T. Attributes—Aware Deep Music Transformation. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR), Montreal, QC, Canada, 11—16 October 2020; pp. 670-677.

[78] Huo, X. Design of Automatic Generation Model for Teaching Music Based on User Differences and Transfer Learning. Int. J. Embed. Syst. 2024 , 17 , 62-72.

[79] Nakamura, T.; Nakamura, E.; Sagayama, S. Acoustic Score Following to Musical Performance with Errors and Arbitrary Repeats and Skips for Automatic Accompaniment. In Proceedings of the 10th Sound and Music Computing Conference, Stockholm, Sweden, 30 July—2 August 2013; pp. 299-304.

[80] Park, J.; Cancino—Chacon, C.; Chiruthapudi, S.; et al. Matchmaker: An Open—Source Library for Real—Time Piano Score Following and Systematic Evaluation. In Proceedings of the 26th International Society for Music Information Retrieval Conference (ISMIR 2025), Daejeon, Korea, 21—25 September 2025.

[81] Wu, Y.; Gardner, J.; Manilow, E.; et al. The Chamber Ensemble Generator: Limitless High—Quality MIR Data via Generative Modeling. arXiv 2022 , arXiv:2209.14458.

[82] Cancino—Chacon, C.E.; Grachten, M.; Goebl, W.; et al. Computational Models of Expressive Music Performance: A Comprehensive and Critical Review. Front. Digit. Humanit. 2018 , 5 , 25.

[83] Oore, S.; Simon, I.; Dieleman, S.; et al. This Time with Feeling: Learning Expressive Musical Performance. Neural Comput. Appl. 2020 , 32 , 955-967.

[84] Ramoneda, P.; Eremenko, V.; D’Hooge, A.; et al. Towards Explainable and Interpretable Musical Difficulty Estimation: A Parameter—Efficient Approach. In Proceedings of the 25th International Society for Music Information Retrieval Conference, San Francisco, CA, USA, 10—14 November 2024.

[85] Gohel, P.; Singh, P.; Mohanty, M. Explainable AI: Current Status and Future Directions. arXiv 2021 , arXiv:2107.07045.

[86] McPherson, G.E.; Thompson, W.F. Assessing Music Performance: Issues and Influences. Res. Stud. Music Educ. 1998 , 10 , 12-24.

[87] Väkevä, L.; Partti, H. Generative AI as a Collaborator in Music Education: An Action—Network Theoretical Approach to Fostering Musical Creativities. Action Crit. Theory Music Educ. 2025 , 24 , 16-52.

[88] Briot, J.P.; Hadjeres, G.; Pachet, F.D. Deep Learning Techniques for Music Generation—A Survey ; Springer: Cham, Switzerland, 2020.

[89] Cope, D. Computer Models of Musical Creativity ; MIT Press: Cambridge, MA, USA, 2005.

[90] Kingma, D.P.; Welling, M. Auto—Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR), Banff, AB, Canada, 14—16 April 2014.

[91] Chu, H.; Urtasun, R.; Fidler, S. Song from PI: A Musically Plausible Network for Pop Music Generation. arXiv 2016 , arXiv:1611.03477.

[92] Manovich, L. AI Aesthetics ; Strelka Press: Moscow, Russia, 2018.

[93] Salganik, R.; Tu, T.; Chen, F.Y.; et al. MusicSem: A Semantically Rich Language—Audio Dataset of Organic Musical Discourse. In Proceedings of the AI for Music Workshop in the 39th Annual Conference on Neural Information Processing Systems, San Diego, CA, USA, 7 December 2025.

[94] Fiebrink, R.A.; Caramiaux, B. The Machine Learning Algorithm as Creative Musical Tool. In The Oxford Handbook of Algorithmic Music ; Oxford University Press: Oxford, UK, 2018.

[95] Ramoneda, P.; Suzuki, M.; Maezawa, A.; et al. Difficulty—Aware Score Generation for Piano Sight—Reading. arXiv 2025 ,arXiv:2509.16913.

[96] Ou, L.; Zhao, J.; Wang, Z.; et al. Unifying Symbolic Music Arrangement: Track—Aware Reconstruction and Structured Tokenization. In Proceedings of the Thirty—Ninth Annual Conference on Neural Information Processing Systems, San Diego, CA, USA, 2—7 December 2025.

[97] Ramoneda, P.; Parada—Cabaleiro, E.; Jeong, D.; et al. Difficulty—Controlled Simplification of Piano Scores with Synthetic Data for Inclusive Music Education. arXiv 2025 ,arXiv:2511.16228.

[98] Bauer, W.I. Music Learning Today: Digital Pedagogy for Creating, Performing, and Responding to Music , 2nd ed.; Oxford University Press: Oxford, UK, 2020.

[99] Ruthmann, S.A.; Hebert, D.G. Music Learning and New Media in Virtual and Online Environments. In The Oxford Handbook of Music Education ; Oxford University Press: Oxford, UK, 2012; Volume 2.

[100] Fiebrink, R. Machine Learning Education for Artists, Musicians, and Other Creative Practitioners. ACM Trans. Comput. Educ. 2019 , 19 , 1-32.

[101] Ilkka, T. The Impact of Artificial Intelligence on Learning, Teaching, and Education ; European Union: Luxembourg, 2018.

[102] Herremans, D.; Chew, E. MorpheuS: Generating Structured Music with Constrained Patterns and Tension. IEEE Trans. Affect. Comput. 2017 , 10 , 510-523.

[103] Luckin, R.; Holmes, W. Intelligence Unleashed: An Argument for AI in Education ; UCL Knowledge Lab.: London, UK, 2016.

[104] Baker, R.S.; Inventado, P.S. Educational Data Mining and Learning Analytics. In Learning Analytics: From Research to Practice ; Springer: New York, NY, USA, 2014; pp. 61-75.

[105] Hallam, S.; Creech, A.; McQueen, H. Can the Adoption of Informal Approaches to Learning Music in School Music Lessons Promote Musical Progression? Br. J. Music Educ. 2017 , 34 , 127-151.

[106] Van Leeuwen, A.; Janssen, J. A Systematic Review of Teacher Guidance During Collaborative Learning in Primary and Secondary Education. Educ. Res. Rev. 2019 , 27 , 71-89.

[107] Zeide, E. The Structural Consequences of Big Data—Driven Education. Big Data 2017 , 5 , 164-172.

[108] Williamson, B. Big Data in Education: The Digital Future of Learning, Policy and Practice ; SAGE Publications Ltd.: London, UK, 2017.

[109] Reich, J.; Ito, M.; Team, M. From Good Intentions to Real Outcomes. Digit. Media Learn. Res. Hub. 2017 . Available online: https://clalliance.org/publications/good—intentions—real—outcomes—equity—designlearning—technologies (accessed on 12 February 2026).

[110] Cetindamar, D.; Kitto, K.; Wu, M.; et al. Explicating AI Literacy of Employees at Digital Workplaces. IEEE Trans. Eng. Manag. 2022 , 71 , 810-823.