Artificial Empathy? AI’s Role in Amplifying (or Erasing) Queer Identities in Music Education
Abstract
Abstract This chapter interrogates how generative and data-driven artificial intelligence (AI) reshape the conditions of visibility for queer youth in music education. Drawing on queer pedagogy, it frames AI as a cultural text whose training data and interfaces encode cis/heteronormative assumptions that can misrecognize or erase LGBTQIA+ students. After situating these risks within long-standing gendered norms across choir, band, orchestra, and curriculum, the chapter synthesizes evidence on algorithmic moderation and recommendation systems, showing how platform logics constrain discovery while professing neutrality. Countervailing possibilities are also mapped: human–AI co-creation, critical playlisting, and community-rooted composition that cultivate empathy and agency. Building from an empathic pedagogy, the chapter proposes a queer-affirming AI curriculum with concrete strategies for general music, choir, and instrumental ensembles, emphasizing refusal, data critique, and “third-space” collaboration. It concludes by positioning music educators as ethical stewards who must bend AI toward solidarity and belonging rather than efficiency and surveillance.
