Music Education with Artificial Intelligence for Inclusive and Sustainable Early Childhood Learning - Margarita Roldan-Cardona; Marcos Chacón-Castro; Janio Jadán-Guerrero; Luis Salvador-Ullauri; Patricia Acosta-Vargas (Emerging Science Journal, 2025)
This study aims to evaluate the impact of a didactic strategy that incorporates generative artificial intelligence (AI) into music education, supporting oral language development in preschool children and promoting inclusive and sustainable early childhood learning. Using an action-research approach, a mixed-methods design was applied to assess the performance of 15 children aged 3 to 6 years, divided into experimental and control groups. The experimental group participated in AI-supported activities using tools such as Genially, Educaplay, and Wordwall, whereas the control group employed traditional methods. Quantitative data from pre-and post-tests, as well as qualitative observations, revealed that AI-enhanced sessions improved motivation, pronunciation, and engagement, particularly among children aged 5 and 6 years old. Although statistical tests showed no significant differences between groups, the intervention demonstrated pedagogical effectiveness by increasing interest and participation. The novelty of this work lies in applying generative AI in early music education to personalize learning and reduce inequality, aligning with several Sustainable Development Goals (SDGs 3, 4, 9, and 10). The findings offer valuable insights into designing inclusive educational experiences through the integration of ICT and AI, highlighting the need to enhance teacher training in emerging digital pedagogies and promote accessible music-based learning in diverse educational settings.