Artificial intelligence (AI) tools are becoming increasingly common in music education, offering new possibilities for vocal training, feedback, and creative exploration. Recent studies have highlighted AI’s potential to improve pitch accuracy, enhance creativity (Liu & Guo, 2025; Gai, 2025), support emotional expressivity (Gai, 2025), and increase practice efficiency (Wang & Gan, 2025). However, these investigations have largely focused on learning outcomes, with limited attention to how students experience these tools as singers. In particular, little is known about how AI-assisted learning shapes students’ vocal identity and sense of self.Vocal identity remains an open concept with no unified definition. Sweet and Parker (2018) described it as a process of becoming individualized within musical contexts. Monks (2003) emphasized the link between vocal self-awareness and identity, while Turpin (2022) highlighted the role of voice and body as expressive vehicles of self-image. In this study, vocal identity is understood as a student’s evolving sense of their own singing voice, shaped by how they perceive, value, and express their vocal sound in relation to social norms, institutional expectations, and technological feedback. This concept includes both the self-perception of vocal tone and ability, and the emotional and cultural meanings that students attach to their voices as they navigate what it means to be a “singer” within AI-mediated learning environments.This study examines how AI-assisted vocal learning tools influence students’ perceptions of their own voice and singing identity. Framed by Bourdieu’s (1984) theory of cultural capital and symbolic power, the research considers how algorithmic feedback may reinforce particular vocal ideals while downplaying others. Rather than focusing on technical outcomes, the study investigates how students’ vocal identity is shaped through interactions with AI tools, including the institutional expectations and internalized norms of what a “good” voice should sound like. Using a qualitative case study approach, this study involves semi-structured interviews with six to eight undergraduate or graduate voice majors who have engaged with AI-based vocal learning technologies. Data will be analyzed thematically (Braun & Clarke, 2006), guided by Bourdieu’s (1984) theoretical framework, with particular attention to the concepts of habitus, cultural capital, and field. This framework will support an examination of how institutional and technological forces intersect in the shaping of students’ vocal identity.