This study investigates how AI tools can enhance students’ musical knowledge, language expression, and digital literacy through interdisciplinary curriculum design. Drawing on students’ prior knowledge from music learning, the curriculum integrated creativity with hands-on practice and positioned AI as a medium for teaching. The course transformed students’ self-descriptive essays into musical works, showcasing the potential of interdisciplinary learning.The curriculum was implemented through an action research approach, following the iterative cycle of implementation, observation, reflection, and revision to enable continuous refinement of instructional content. Centered on music learning and creative practice, it fostered students’ integrated development across cognitive and expressive domains, while addressing key challenges in the classroom such as the abstract nature of musical form concepts, the lack of creative motivation in traditional teaching, and insufficient interdisciplinary integration.The curriculum was structured around music learning and creative practice, with students’ personal writings transformed into musical works to highlight the diversity and applied potential of interdisciplinary learning. It was implemented over six 40-minute lessons with fifth-grade students, and organized into four stages. In the first stage, students developed foundational concepts of classical musical forms to build structural awareness. The second stage extended to popular music analysis, enabling students to compare different forms and deepen their understanding. The third stage focused on creative scene-based music, where students employed elements such as music tempo, key, and timbre, combined with scene descriptions, and used Suno AI to generate context-appropriate music. In the fourth stage, students integrated language and music by composing self-reflective essays, which were transformed the text into popular song lyrics through generated AI, refined rhetorically and structurally, and set into sections aligned with popular song forms before being generated as original compositions with Suno. Data collection included student works, worksheets, and feedback on Padlet, supplemented by evaluation checklists to guide curriculum improvement and assess learning outcomes.The findings reveal that AI tools not only enriched students’ understanding of musical forms but also enabled them to apply abstract theoretical concepts in their own compositions. Students created music that reflected their personal ideas, demonstrating strong engagement, creativity, and interdisciplinary competence. Peer review and collaborative sharing further enhanced learning motivation and self-efficacy. Overall, the integration of AI technology with music theory, language expression, and digital tools provided a concrete model for interdisciplinary curriculum design, opening new possibilities for music education and highlighting the transformative potential of AI-assisted pedagogy.