AI evidence extraction

Musicology and artificial intelligence

Philippe Vendrix, Rebekah Ahrendt · HAL (Le Centre pour la Communication Scientifique Directe) · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.5281/zenodo.20639784

Methodology & findings

Study design

Narrative review and conceptual mapping of AI applications in European musicological research, with thematic organization across six complementary domains including methodological foundations, data infrastructure, educational implications, and ethical best practices..

Main result

The report maps "the affordances and limitations of AI-based models and tools for the discipline, while situating them within the European normative landscape defined by the ALLEA European Code of Conduct for Research Integrity, the EU Artificial Intelligence Act, and the UNESCO Recommendation on the Ethics of Artificial Intelligence." The study addresses six complementary areas including methodological foundations for audio-, image-, encoding-, and language-model applications; data infrastructure governance; disciplinary stakes; musicological education redesign; professional practices and responsibilities; and best practices emphasizing "methodological pluralism against technological determinism."

Reports effect sizes.

Research paradigm

Interpretivist/normative analysis

Author conclusions

The report articulates that best practices should formalize "principles of ethical reflexivity, human oversight, explainability, reproducibility, and accountability" while maintaining "methodological pluralism against technological determinism." The authors position their framework as addressing musicologists, librarians, archivists, curators, educators, research-policy officers, infrastructure coordinators, and tool developers across the discipline.

Open questions raised

The report implicitly identifies gaps in: (1) understanding AI affordances and limitations for musicological research; (2) data governance and infrastructure standardization in European musicology; (3) curricular integration of AI literacy in musicological education; (4) professional responsibility frameworks for AI use in musicology; and (5) ethical reflexivity practices to counter technological determinism.
The report identifies gaps in: (1) understanding affordances and limitations of AI tools for musicology; (2) data governance and infrastructure standardization (FAIR/CARE principles, ontologies); (3) disciplinary implications of source and corpus processing; (4) AI literacy and curricular redesign in musicological education; (5) professional responsibilities, intellectual property frameworks, and institutional risk management; and (6) ethical frameworks balancing technological innovation with disciplinary integrity.
The report identifies gaps across multiple domains: representational bias, quality variability, and copyright concerns in data infrastructure; risks of fragmentation in European federated infrastructures; curricular redesign needs in musicological education; and institutional and legal risks in professional practice.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 75%

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