Research

AI Virtual Singers are increasingly important in music production and human–AI collaboration, yet limited research has examined user perception and acceptance in practical creative contexts. Drawing on the Technology Acceptance Model (TAM), this mixed-methods study explores factors associated with user acceptance and perceived value among 378 music practitioners, educators and enthusiasts—predominantly from Chinese-speaking (94.7%) communities. Quantitative analyses included chi-square tests, correlation analyses, reliability assessments, and comparative analyses. Qualitative insights were obtained through sentiment analysis and thematic examination of open-ended responses. Technical familiarity was associated with higher levels of acceptance, while perceived simulation fidelity showed a positive relationship with expectations of future impact. Participants prioritized functional value over cultural value in production contexts, although cultural identity remained relevant to community engagement. Qualitative findings revealed a tension between realism and the distinctive “electronic timbre” valued by some users. Rather than validating a complete TAM model, this study provides exploratory evidence regarding factors relevant to AI Virtual Singer acceptance, contributing to emerging discussions on human–AI co-creation, creative technology adoption, and the evolving role of AI-generated voices in contemporary music production.

Keywords: AI Virtual Singer; Human–AI Co-Creation; Technology Acceptance Model; Music Production; Singing Voice Synthesis; Mixed Methods