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Publication
Generative al in generalist teacher training: New possibilities for musical creation
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| 1.95 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Artificial Intelligence (AI) revolutionizes music education by analyzing patterns, identifying structures, and offering creative suggestions that enhance composition and arrangement skills (Vasco & Eguiguren, 2023; Caudeli & González, 2024). This new panorama enriches musical educational experiences (Holland, 2000; Li & Wang, 2024), allowing democratic access beyond classroom limitations (Cheng, 2025) and enabling personalize instruction aligned with students’ individual learning rhythms,
stylistic preferences, and educational goals (Yu et al., 2023; Olvera-Fernández et al., 2023). However, these environments do not inherently guarantee creative use and raise ethical concerns about plagiarism. When students engage with AI in a predominantly informative and pragmatic way, seeking ready-made outputs like melodies or accompaniments, they often overlook the creative process and the origin of generated content. This limited engagement increases the likelihood of unintentional plagiarism and undermines deeper musical learning, as students may reproduce materials without critical appropriation or understanding of authorship (Morreale, 2021; Merchán Sánchez-Jara et al., 2024). Based on the research question:
“What is the impact of AI tools in the development of musical competencies in generalist teachers?” this study investigates an AI-assisted educational
intervention in music training of generalist teachers involving eighty-five students of the 3rd year of Primary Education from a Faculty of Education in
Spain. The methodology integrated several qualitative research instruments: data collection through an ad hoc initial questionnaire, direct observation during the intervention, analysis of documents, and students-created artistic-sound productions using AI, as well as interpretation of the results following the pedagogical process. The intervention comprised three phases: 1) a theoretical presentation of the historical evolution of generative AI in music; 2) a demonstrative exploration of the pedagogical and artistic potential of the tool by manipulating different sound parameters; and 3) a practical exploration that allowed participants to create collaborative compositions. Eighteen creative projects involving AI-assisted composition were analyzed. These works demonstrated elements such as timbre, rhythmic structures, textures, intensity, sound-image relationships, and soundscape creation. Student group descriptions of their compositional processes enriched the analysis, offering insights into their decision-making and interaction with AI tools. Results indicated the acquisition of various musical and expressive skills. In several cases, students demonstrated an intention to correlate musical parameters (e.g., texture, timbre, density) with visual representation. Findings also suggest the value of exploring interdisciplinary links between music and other arts through technology. Furthermore, transversal competencies such as digital literacy, creativity, critical thinking, and collaboration were merged during the process.
Research on AI in educational contexts – particularly music education – remains scarce (Zhang et al., 2024). In this sense, documenting its effects on the training of generalist teachers by collecting evidence on how it is used by students and its impact on the development of musical skills proves to be an important contribution to innovation in pedagogy. Human interaction remains fundamental to successful musical learning; however, AI can complement and enrich this process by providing students with additional resources and practice opportunities.
Description
Keywords
Generative AI Pedagogical Innovation Music Creation Generalist Teacher Training Higher Education
Pedagogical Context
Citation
Nascimento, Gerson Rafael; Blanco García, Yurima; Cardoso, Mário (2025). Generative al in generalist teacher training: New possibilities for musical creation. In II Congreso Internacional Universitario. Valladolid: Universidad. P. 55-57
Publisher
Universidad de Valladolid