Musical Giftedness in Human-AI Creation: Interpreting Evidence Beyond the Finished Product

Generative artificial intelligence complicates a central problem in identifying musical giftedness: deciding which features of an outcome can reasonably be attributed to the student who presents it. A piece may display coherent formal design, an elaborate harmonic plan, idiomatic instrumentation, or convincing timbral balance even when those decisions were neither made nor understood by the learner. This conceptual article examines how music co-created with AI can be interpreted when the finished product no longer reveals the student’s contribution on its own. Drawing on research on musical talent development, AI-assisted creation, and assessment validity, it considers three sources of information: compositional problem framing, analytical listening and revision, and the traceability of musical decisions. Transfer across tasks provides a further check. These sources are not diagnostic criteria. They are intended to support more cautious interpretations of musical evidence in AI-mediated creative work and to complement broader developmental evidence used in identification processes.