Needs Analysis of AI-Assisted Graphic Notation Design in Kindergarten Music Activities: A Preliminary Study from Teachers’ Perspectives

With the rapid development of educational digitalization and generative artificial intelligence technologies, AI-assisted teaching has gradually entered different educational contexts, including early childhood education. In kindergarten music activities, graphic notation, as a visual representation tool of rhythm, melody, and musical structure, has been increasingly valued because it fits the concrete and image-based thinking characteristics of young children. Compared with traditional notation systems, graphic notation can help children understand abstract musical concepts more intuitively, improve participation in music activities, and enhance musical perception and expression. However, in actual teaching practice, many kindergarten teachers still encounter difficulties in designing and applying graphic notation, including insufficient design experience, lack of suitable teaching resources, time-consuming production processes, and limited digital design abilities. Against this background, this study focuses on kindergarten teachers’ needs for AI-assisted graphic notation design in music activities. As a preliminary investigation conducted during the early stage of a larger doctoral research project on AI-supported graphic notation teaching, the study aims to explore teachers’ current use of graphic notation, their attitudes toward AI-assisted design, and their expectations regarding AI-related functions in kindergarten music education. A questionnaire survey was conducted among 126 kindergarten teachers from eastern and central regions of China, and descriptive statistical analysis was carried out using SPSS 26.0. The findings indicate that kindergarten teachers generally hold positive attitudes toward AI-assisted graphic notation design. Teachers believe that AI technology may help reduce preparation time, lower the difficulty of graphic notation production, and provide richer teaching resources for music activities. The most expected functions include automatic generation of graphic notation based on music input, intelligent matching of child-friendly visual symbols, generation of supporting music activity plans, and one-click production of PPT or courseware materials. At the same time, teachers also expressed concerns regarding the child suitability, educational appropriateness, and editability of AI-generated content. This study provides preliminary empirical support for the future development of AI-assisted graphic notation teaching models in kindergarten music education. It also enriches research on the integration of artificial intelligence, multimodal teaching, and early childhood music education under the background of educational digital transformation.