Integrating computational intelligence in music teaching: a decision-making approach for personalised education

With the integration of computational intelligence (CI) in music education, the traditional teaching paradigms have been transformed, and personalised and adaptive learning experiences have been offered. This paper describes a methodology for making decisions on music education, which is based on artificial intelligence (AI), machine learning (ML) and data analytics. The AI-powered CI frameworks provide tailored instructional content, optimised practice regimes, and instant feedback through analytics of students' abilities, preferences, and evolution. In this study, a critical review of existing CI techniques in music education has taken place intending to investigate their effectiveness in the areas of skill acquisition, engagement, and curriculum design. Furthermore, to increase the levels of personalisation in learning pathways, a novel decision-making model is proposed that ensures students receive instruction tailored to their abilities and goals. The experimental validation of the proposed methodology was carried out through case studies, demonstrating its effectiveness in improving student outcomes and learning processes within educational contexts. The study's conclusions reinforce the idea that the power of CI can transform music pedagogy from educational to Artificial Intelligence. The results indicate that AI-powered education is the way to develop learning opportunities in future programs.