Recent Research Discovery
Current Research Direction
> AI Virtual Singers and Human–AI Co-Creation
Investigating how AI-mediated vocal technologies influence creative practices, perceived value, and human–AI collaboration.
> Digital Cultural Identity
Exploring AI virtual singers as emerging digital cultural icons and examining how synthetic voices participate in cultural representation and identity formation.
> Audience Experience & Affective Computing
Examining emotional engagement, perceived authenticity, and audience responses to AI-generated voices using behavioral and physiological measures.
> AI in Music Education
Investigating the pedagogical potential of AI virtual singers through classroom-based intervention studies.
Research Projects
Master’s Thesis:
YU, J., LI, H., & Macau University of Science and Technology. Faculty of Humanities and Arts . (2025). User Perception and Acceptance of AI Virtual Singers in Music Creation and Education: A Study on Impact and Application YU, JIN. [Macau University of Science and Technology].
Manuscripts Accepted and Published
Yu, J., & Li, J. (2026). A TAM-informed exploration of user acceptance of AI virtual singers in human–AI co-creation: simulation fidelity, functional value, and cultural value. Cogent Arts & Humanities, 13(1).
Cogent Arts & Humanities is a CiteScore Best Quartile Q1 ESCI journal.
◆ Simulation fidelity significantly correlates with users’ future expectations (r=0.450)
◆ Identified the “electronic timbre paradox”—some users value synthetic qualities as cultural markers
◆ Functional value (efficiency) outweighs cultural value (IP) in East Asian creator ecosystems
◆ Extended TAM with fidelity and value dimensions for creative AI contexts
Keywords: AI Virtual Singer; Human–AI Co-Creation; Technology Acceptance Model; Music Production; Singing Voice Synthesis; Mixed Methods
Manuscripts Ready for Submission
Yu, J., & Chang, Y. (Manuscript ready for submission). Integrating Synthesizer V AI Virtual Singers
into University Music Classrooms: A Mixed-Methods Classroom Intervention Study.
◆ High Acceptance & Outcomes: Students reported strong perceived usefulness (M=4.41) and ease of use (M=4.40), with project performance averaging 86.44/100.
◆ Cognitive Load Insight: 89.8% of students reported no or minor cognitive burden, supporting AI as an “intelligent scaffold” for creative tasks.
◆ Accessibility–Complexity Trade-off: Advanced expressive parameters scored lower, revealing the need for graduated instruction in AI music pedagogy.
Keywords: Synthesizer V; AI in Music Education; Technology Acceptance Model (TAM); Cognitive Load Theory (CLT); Human-AI Collaboration; Higher Education