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2026
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| Online Access: | https://doi.org/10.5281/zenodo.19350459 |
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| author | Chatzigiannakis, Dimitri Papadopoulou, Agnes |
| author_facet | Chatzigiannakis, Dimitri Papadopoulou, Agnes |
| contents | <p>The integration of Artificial Intelligence (AI) in music education has emerged as a transformative development, offering new tools and opportunities to learners and educators. This paper explores the advantages and limitations of adopting AI technologies in music education, with a focus on promoting access and equity. AI applications such as music composition software, virtual mentors, and adaptive online platforms have revolutionized traditional teaching methods, enabling personalized learning experiences and greater accessibility for students in underserved communities. Programs like Yousician, AIVA, and Amper Music exemplify how AI tools are transforming music education by providing real-time feedback and adaptive instruction.<br>Despite these advancements, the use of AI raises significant concerns, privacy issues related to data collection and the potential displacement of human educators are central challenges. AI systems rely heavily on data, and if this data mainly reflects Western musical traditions, it risks sidelining the rich and diverse musical heritage of other cultures. This could lead to a world where students are exposed to only a narrow slice of global music, potentially erasing the unique sounds and stories that non-Western traditions bring to the table. Furthermore, the reliance on proprietary technologies may exacerbate inequalities, limiting access to students and educators in resource-constrained settings. Open-source solutions, however, offer a promising alternative, promoting democratization and inclusivity in education.<br>Through the lens of A/r/tography—a research methodology emphasizing the integration of art, research, and teaching (Chatzigiannakis, 2022)—this paper investigates how AI can inspire innovative teaching strategies while safeguarding the human-centered values of creativity and equity. The concept of Homo aestheticus (Dissanayake, 1995) suggests that artistic engagement is an inherent human trait, not an accessory but a fundamental part of meaning-making. AI must, therefore, enhance rather than diminish the deeply rooted, evolutionary role of art in human development. While AI holds immense potential to enhance music education, its adoption must be approached with caution, prioritizing ethical principles and inclusivity to ensure a balanced and equitable impact on learners and educators. A/r/tography’s emphasis on integrating artistic and pedagogical practices allows this study to critically evaluate how AI impacts both access and the creative dimensions of music education. This approach provides a nuanced perspective on balancing technological innovation with inclusivity.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19350459 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Artificial intelligence and the Democratization of Music Ai-[e]ducation Chatzigiannakis, Dimitri Papadopoulou, Agnes Artificial intelligence Music Education Technology for Education Arts-based Educational Research <p>The integration of Artificial Intelligence (AI) in music education has emerged as a transformative development, offering new tools and opportunities to learners and educators. This paper explores the advantages and limitations of adopting AI technologies in music education, with a focus on promoting access and equity. AI applications such as music composition software, virtual mentors, and adaptive online platforms have revolutionized traditional teaching methods, enabling personalized learning experiences and greater accessibility for students in underserved communities. Programs like Yousician, AIVA, and Amper Music exemplify how AI tools are transforming music education by providing real-time feedback and adaptive instruction.<br>Despite these advancements, the use of AI raises significant concerns, privacy issues related to data collection and the potential displacement of human educators are central challenges. AI systems rely heavily on data, and if this data mainly reflects Western musical traditions, it risks sidelining the rich and diverse musical heritage of other cultures. This could lead to a world where students are exposed to only a narrow slice of global music, potentially erasing the unique sounds and stories that non-Western traditions bring to the table. Furthermore, the reliance on proprietary technologies may exacerbate inequalities, limiting access to students and educators in resource-constrained settings. Open-source solutions, however, offer a promising alternative, promoting democratization and inclusivity in education.<br>Through the lens of A/r/tography—a research methodology emphasizing the integration of art, research, and teaching (Chatzigiannakis, 2022)—this paper investigates how AI can inspire innovative teaching strategies while safeguarding the human-centered values of creativity and equity. The concept of Homo aestheticus (Dissanayake, 1995) suggests that artistic engagement is an inherent human trait, not an accessory but a fundamental part of meaning-making. AI must, therefore, enhance rather than diminish the deeply rooted, evolutionary role of art in human development. While AI holds immense potential to enhance music education, its adoption must be approached with caution, prioritizing ethical principles and inclusivity to ensure a balanced and equitable impact on learners and educators. A/r/tography’s emphasis on integrating artistic and pedagogical practices allows this study to critically evaluate how AI impacts both access and the creative dimensions of music education. This approach provides a nuanced perspective on balancing technological innovation with inclusivity.</p> |
| title | Artificial intelligence and the Democratization of Music Ai-[e]ducation |
| topic | Artificial intelligence Music Education Technology for Education Arts-based Educational Research |
| url | https://doi.org/10.5281/zenodo.19350459 |