Chinese User Needs Identification and Functional Priority Analysis of Chinese AI-Assisted Music Education Systems

초록

With the increasing adoption of intelligent technologies in music education, AI-assisted music-learning systems are widely used for skill training and learning support; however, their functional configurations remain insufficiently grounded in empirically validated user-need structures. From the learner’s perspective, this study systematically identifies user requirements for AI-assisted music education systems and determines their functional priorities. Sixteen core user needs (H1–H16) were extracted through semi-structured interviews and thematic analysis, followed by a KANO survey (426 valid responses) to classify need attributes, and an Analytic Hierarchy Process (AHP) with twelve domain experts to calculate priority weights. At the criterion level, basic needs received the highest weight (0.5816), substantially exceeding performance (0.3090) and attractive needs (0.1095), indicating that system reliability and usability constitute fundamental prerequisites for user acceptance. At the functional level, feedback accuracy (H5) ranked first with the greatest composite weight (0.23175), followed by improvement guidance (H7, 0.15610) and feedback interpretability (H6, 0.10620), jointly forming a core feedback mechanism that directly supports effective music learning. Practice organization, difficulty adaptation, and learning monitoring (H9–H11) showed comparable weights (0.06275 each), reflecting stable demand for structured and continuous learning support. Among attractive needs, the incentive mechanism (H14) exhibited the highest priority (0.06187), surpassing emotional experience, expressive space, and creativity support. These findings suggest that AI-assisted music education systems should first ensure accurate and actionable feedback as the foundation of learning effectiveness, and only subsequently enhance practice organization and incentive mechanisms to sustain long-term engagement, thereby providing a prioritized and evidence-based framework for system design and optimization.

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