Special Issue: Artificial Intelligence and Music Education

Editors: adam patrick bell, Ran Jiang, & Mark Daley (Western University)

The widespread use of ChatGPT, DALL·E, and Stable Diffusion has sparked worldwide discussions about how artificial intelligence (AI) can alter humans’ learning, working, thinking, and lives in general. To date, most literature on AI and music teaching and learning has been produced by researchers outside of the music education profession. For example, the 1993 World Conference on Artificial Intelligence in Education hosted a day of workshops in which computer science researchers proposed various interdisciplinary theories and approaches focused on music education (Smith et al. 1994). More recent research related to music education by computer scientists has examined the effectiveness of AI applications in teaching students how to sing or play an instrument (Cui 2022; Delgado et al. 2018; Ince et al. 2021; Li and Wang 2023), assisting with music teaching (Han et al. 2023), facilitating composing and musicking (Cook 1994; Dahlstedt 2021; Eldridge 2022; Franklin 2006; Moruzzi 2018; Schöen and Tompits 2022; Tsuchiya and Kitahara, 2019), promoting improvisation (Addessi and Pachet 2005), assisting with music sight reading (Pierce et al. 2021), and supporting dyslexic learners in music learning contexts (Ventura 2019).

This body of research exemplifies the interests AI researchers have in music education, but what interests do music education researchers have in AI? We forward that it is imperative that music education researchers grapple with the implications of AI in/for/against/as/around music education.

The aim of this special issue is to critically examine intersections of AI and music education. We invite authors to consider the role(s) of AI in music education from diverse theoretical, critical, and philosophical perspectives. We offer the following prompts:

1. How is artificial intelligence experienced in music education by teachers and learners in schools and/or community contexts? How should artificial intelligence be experienced in music education by teachers and learners?
2. How can artificial intelligence mediate the ways in which teachers and learners engage in music making?
3. How can artificial intelligence influence* music educators’ pedagogical philosophies?  *ChatGPT recommends the following terms in place of “influence” for this prompt: enhance, augment, transform, challenge, inform, disrupt, facilitate, integrate, inspire, question, shape, customize, analyze, simulate, and revolutionize.
4. How can artificial intelligence intersect with music education as it relates to teachers’ and learners’ identities, including but not limited to disability, sexuality, gender, race, ethnicity, class, religion, and culture?
5. How can AI shift, if at all, understandings of musical ability, aptitude, expertise, intelligence, talent, and other related constructs? What are the implications for music education?
6. What potential ethical issues could arise from the integration of artificial intelligence in music education, broadly conceived? How should music educators adapt their practices and policies to address issues including but not limited to agency, authorship, creativity, equity, intellectual property, and labor?
7. In what ways can artificial intelligence interact with the practices and/or processes of accompanying, arranging, collaborating, composing, improvising, producing, songwriting, and other ways of musicking?

Beyond or in addition to these prompts, potential contributors to this special issue may also benefit from engaging with Goodlad’s (2023) framing of the “three core dilemmas for critical AI studies” and considering them within the context of music education:

1. Reductive and Controversial Meanings of ‘Intelligence’;
2. Problematic Benchmarks and Tests for Supposedly Scientific Terms Such as ‘AGI’ [Artificial General Intelligence]; and
3. Bias, Errors, and Concentration of Power.

Finally, for potential contributors who find that none of the aforementioned prompts satiate their interest in AI and music education, we welcome you to generate your own prompts with or without the assistance of AI.

Submission Deadline

Please submit your manuscript as a Word document via e-mail, no later than May, 1 2024 to adam patrick bell at adam.bell@uwo.ca, copied to the ACT Editor Lauren Kapalka Richerme at lkricher@indiana.edu.

Submission Guidelines

Action, Criticism, and Theory for Music Education is devoted to the critical study and analysis of issues related to the field of music education. ACT welcomes submissions from diverse perspectives (e.g. education, music, philosophy, sociology, history, psychology, curriculum studies), dealing with critical, analytical, practical, theoretical, or policy development concerns, as well as submissions that seek to apply, challenge, or extend the MayDay Group’s Action Ideals.

Article Length

ACT imposes no set restrictions on length. However, authors may be asked to shorten submissions where reviewers or the editor determine that an essay’s length is not warranted by its content.

Formatting

Please format submissions using the most recent edition of the Chicago Manual of Style’s “author-date system” with the following three adaptations: 1) omit quotations marks around titles in reference lists, 2) follow APA conventions for capitalization in reference lists, and 3) use closed ellipses (necessary for html formatting). Endnotes are permitted. Audio and video materials are encouraged. Also, ACT encourages the use of “they” (and any derivation) as a singular, gender-neutral pronoun. Consult a recent issue of ACT or contact the editors for more information if required.

Abstract and Keywords

Submissions must be accompanied by a brief abstract (ca. 100–150 words) and a short list of keywords.

About the Author

Include a 100–150-word biography for each author.

Languages

Following ACT’s special issue guidelines on the Decolonization of Music Education, and with the purpose of actively diversifying knowledge creation strategies, this special issue also welcomes manuscripts that were originally published in a language other than English and in a venue not commonly accessible by all. Submitters are required to provide the English translation of their submissions.

Peer Review Process

ACT submissions are subject to a rigorous process of anonymized peer review. Final publication decisions rest with the editors (in consideration of reviewer recommendations).

References

Addessi, Anna Rita, and Francois Pachet. 2005. Young children confronting the Continuator, an interactive reflective musical system. Musicae Scientiae 10 (1): 13–39.

Cook, J. 1994. Agent feflection in an intelligent learning-environment architecture for musical composition. In Music Education: An Artificial Intelligence Approach, edited by M. Smith, A. Smaill, and G. A. Wiggins, 3–23, Springer-Verlag.

Cui, Kangxu. 2022. Artificial intelligence and creativity: Piano teaching with augmented reality applications. Interactive Learning Environments: 1–12. https://doi.org/10.1080/10494820.2022.2059520

Dahlstedt, Palle. 2021. Musicking with algorithms: Thoughts on artificial intelligence, creativity, and agency. In Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity, edited by Eduardo Reck Miranda, 873–914. Springer. https://doi.org/10.1007/978-3-030-72116-9_31

Delgado, Miguel, Waldo Fajardo, and Miguel Molina-Solana. 2018. A software tool for categorizing violin student renditions by comparison. In Advances in Artificial Intelligence, CAEPIA 2018, edited by F. Herrera, S. Damas, R. Montes, S. Alonso, O. Cordon, A. Gonzalez, and A. Troncoso, 11160: 330–340. Springer. https://doi.org/10.1007/978-3-030-00374-6_31

Eldridge, Alice Cecelia. 2022. Computer musicking as onto-epistemic playground. Journal of Creative Music Systems 1 (1): 1–31. https://doi.org/10.5920/jcms.1038

Franklin, Judy A. 2006. Recurrent neural networks for music computation. INFORMS Journal on Computing 18, no. 3: 321–38. https://doi.org/10.1287/ijoc.1050.0131

Goodlad, Lauren M. E. 2023. Editor’s introduction: Humanities in the loop. Critical AI 1 (1–2). https://doi.org/10.1215/2834703X-10734016

Han, Xiao, Fuyang Chen, Ijaz Ullah, and Mohammad Faisal. 2023. An evaluation of AI-based college music teaching using AHP and MOORA. Soft Computing. https://doi.org/10.1007/s00500-023-08717-5

Ince, Gökhan, Rabia Yorganci, Ahmet Ozkul, Taha Berkay Duman, and Hatice Köse. 2021. An audiovisual interface-based drumming system for multimodal human–robot interaction. Journal on Multimodal User Interfaces 15 (4): 413–28. https://doi.org/10.1007/s12193-020-00352-w

Li, Ping-ping, and Bin Wang. 2023. Artificial intelligence in music education. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2023.2209984

Moruzzi, Caterina. 2018. Creative AI: Music composition programs as an extension of the composer’s mind. In Philosophy and Theory of Artificial Intelligence 2017, edited by Vincent C. Müller, 69–72, Springer. https://doi.org/10.1007/978-3-319-96448-5_8

Pierce, Charlotte, Tim Hendtlass, Anthony Bartel, and Clinton J. Woodward. 2021. Evolving musical sight reading exercises using expert models. Frontiers in Artificial Intelligence 3. https://www.frontiersin.org/articles/10.3389/frai.2020.497530

Schön, Felix, and Hans Tompits. 2022. PAUL: An algorithmic composer for classical piano music supporting multiple complexity levels. In Progress in Artificial Intelligence, edited by Goreti Marreiros, Bruno Martins, Ana Paiva, Bernardete Ribeiro, and Alberto Sardinha, 415–26. Springer. https://doi.org/10.1007/978-3-031-16474-3_34

Smith, Matt, Alan Smaill, and Geraint A. Wiggins, eds. 1994. Music education: An artificial intelligence approach. Springer-Verlag.

Tsuchiya, Yuichi, and Tetsuro Kitahara. 2019. A non-notewise melody editing method for supporting musically untrained people’s music composition. Journal of Creative Music Systems 3 (1): 1–25. https://doi.org/10.5920/jcms.624

Ventura, Michele Della. 2019. Exploring the impact of artificial intelligence in music education to enhance the dyslexic student’s skills. In Learning Technology for Education Challenges, edited by Lorna Uden, Dario Liberona, Galo Sanchez, and Sara Rodríguez-González, 14–22. Springer. https://doi.org/10.1007/978-3-030-20798-4_2