Evidence map›Paper›PMID 42558248›Full record

ArticleFrontiers in sociology2026

From traditional musicians to digital musicians: a study on talent transformation in the music industries driven by AI technology.

Wei Wang

Abstract read
In one paragraph

Article in Frontiers in sociology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Wei WangSchool of Music, Hanshan Normal University, Chaozhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The rapid integration of artificial intelligence (AI) into the music industries is fundamentally reshaping occupational roles, skill requirements, and career pathways, posing critical challenges for workforce development. This transformation compels a shift from conventional, craftbased music-making toward digitally enabled, interdisciplinary practices. Methods: Employing a phenomenological qualitative research design, this study conducted in-depth, semistructured interviews with 20 stakeholders across four key groups-government officials, higher education faculty, enterprise managers, and frontline musicians- within the Beijing music industry. Data were analyzed using a six-step thematic analysis process facilitated by NVivo 14. Results: The findings reveal that AI is systematically permeating music creation, production, distribution, and copyright management, driving a shift toward more digital, collaborative, and data-informed roles. However, significant cognitive gaps and divergent adaptive capacities among stakeholder groups have created structural imbalances within the talent development ecosystem. Discussion: To address these challenges, this paper proposes an empirically grounded, four-pillar workforce development framework comprising institutional support, educational reform, enterprise engagement, and community development, emphasizing systemic coordination to build a resilient digital music talent ecosystem. This study advances theoretical understanding of labor market transformation in creative industries and provides practical pathways for regions seeking to foster a sustainable and inclusive music workforce in the age of AI.

Indexed as

artificial intelligencemusic industryskill transformationsustainable developmentworkforce development strategy

Identifiers

PMID42558248
PMCPMC13437274

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.