Answers are grounded in this paper and cite their sources.
Mixed-methods study reveals task- and role-specific variations in AI adoption among professional musicians, highlighting the need for granular research designs across occupations.
To illustrate the importance and complexity of considering “task–technology fit”, as well as users’ roles and purposes, when studying AI adoption within any occupation, we present findings from a 2023-25 mixed-method study about professional musicians’ uses and perceptions of AI in music-making. The initial interviews conducted with a diverse group of 42 U.S. musicians suggested that individual levels of interest in and adoption of AI in their music-making depended on the specific task considered, whether the task was perceived as “core” versus “supportive” to one’s professional role, and the intention of using AI to “assist” versus “replace” one’s work. Responses to a subsequent 2025 survey, designed to further explore these insights, as well as to collect additional information about musicians’ AI adoption, confirmed the value of eliciting respondents’ interest, uses, and feelings about using AI for specific tasks rather than in general terms. Exploring the impact of the respondents’ professional roles and purpose proved to be more challenging. Nevertheless, role-based differences were documented, several statistically significant. The study has methodological implications for studies of AI adoptions across fields.
No takes yet. Share an insight, caveat, or question.
Borasi et al. (2026) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: