Perceptions of AI-generated and co-created music among university students and social media users

This study explores how listeners perceive and interpret music created by or with artificial intelligence (AI).Using a custom-designed survey, 57 university students and social media users listened to a series of 12 music tracks (composed by humans, co-created with AI, or fully AI-generated) and attempted to identify the origin of each.Participants also responded to questions about their attitudes toward AI in music.Results revealed that co-created tracks were the most difficult to identify correctly, while AI-only and human-generated tracks had modestly higher identification rates.Musicians and non-musicians showed similar accuracy but differed in confidence and willingness to engage with AI-assisted music.Most participants expressed support for labeling AI-generated music and were cautiously open to AIhuman creative collaboration, though concerns remained regarding authenticity and emotional depth.These findings offer insight into public readiness for AI in the arts and suggest pathways for ethical design, media literacy, and future research in music technology.

Publication Details

Journal
Issues in Information Systems
Published
2025-01-01
DOI
https://doi.org/10.48009/4_iis_2025_110
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
0.00
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article

Perceptions of AI-generated and co-created music among university students and social media users

Issues in Information Systems
Mental Health via Writing
article

Perceptions of AI-generated and co-created music among university students and social media users

article en

Abstract

This study explores how listeners perceive and interpret music created by or with artificial intelligence (AI).Using a custom-designed survey, 57 university students and social media users listened to a series of 12 music tracks (composed by humans, co-created with AI, or fully AI-generated) and attempted to identify the origin of each.Participants also responded to questions about their attitudes toward AI in music.Results revealed that co-created tracks were the most difficult to identify correctly, while AI-only and human-generated tracks had modestly higher identification rates.Musicians and non-musicians showed similar accuracy but differed in confidence and willingness to engage with AI-assisted music.Most participants expressed support for labeling AI-generated music and were cautiously open to AIhuman creative collaboration, though concerns remained regarding authenticity and emotional depth.These findings offer insight into public readiness for AI in the arts and suggest pathways for ethical design, media literacy, and future research in music technology.

Issues in Information Systems
Openalex Percentile: Top 71%
Mental Health via Writing
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