[iberoamericanmusiclist] Summer Readings: Musicology and Artificial Intelligence

Tue Jun 16 02:46:46 PDT 2026

Dear colleagues,

I would like to share the recently published report "Musicology and
Artificial Intelligence", which may be of interest to members of this
community.

Although the report was produced within the framework of the European
Research Area, many of the questions it raises extend far beyond Europe and
are relevant to musicological research in a wide range of academic and
geographical contexts. It offers thoughtful reflections on the challenges
and opportunities that artificial intelligence presents for our field,
including research, teaching, data management, academic ethics, and future
developments.

I hope you will find it useful and that it may contribute to ongoing
discussions about an issue that is quickly reshaping the ways we conduct
research and teach musicology.

*https://zenodo.org/records/20639784 <https://zenodo.org/records/20639784>*

With best wishes,

Javier Marín-López
Chair, Ibero-American Music Study Group
American Musicological Society

***

*[Excerpt from the Introduction]*

This report offers a structured overview of Artificial Intelligence (AI) in
European musicological research. It is addressed primarily to musicologists
— historians, theorists, ethnomusicologists, and analysts — as well as to
librarians, archivists, curators, and educators who are increasingly
confronted with the integration of computational tools into their practice.
It will also be of interest to research-policy officers, infrastructure
coordinators, and tool developers seeking a clearer understanding of the
discipline’s specific requirements.

The report moves from conceptual foundations towards institutional
implications, across six complementary sections:

Section 1: Methodological Foundations. Clarifies the notions of model,
tool, and critical posture, and surveys audio-, image-, encoding-, and
language-model-based applications of AI in musicology.

Section 2: Data Infrastructure: From Digitization to Corpora. Addresses
ethical data governance (FAIR/ CARE), ontologies and semantic
interoperability, European federated infrastructures, and the risks and
opportunities associated with musicological corpora.

Section 3: OMR: Techniques, Systems, and Editorial Challenges. Examines
Optical Music Recognition as a technical, editorial, and epistemological
challenge, with attention to historical sources, collaborative correction,
datasets, multimodal models, tablatures, non-standard notations, and new
musicological questions raised by OMR.

Section 4: Musicological Education in the Age of AI. Considers curricular
redesign, institutional guidelines, AI literacy, and concrete design models
such as the Two-Lane Approach.

Section 5: Professional Practices and Responsibilities. Examines scholarly
credibility, intellectual property, academic integrity, institutional AI
governance, collaborations with the cultural and creative industries, legal
and institutional risks, gender equality, and lessons from organised labour.

Section 6: Towards Best Practices for AI in Musicological Research.
Provides an integrated overview of AI tools, discusses methodological
pluralism versus technological determinism, and formalises ethical
responsibility, human oversight, and academic integrity.

This document is neither a technical programming manual, nor an exhaustive
comparison of commercial software, nor a step-by-step implementation guide.
It does not advocate a single position on the place of AI in musicology: it
favours methodological pluralism and the coexistence of approaches rather
than prescribing one path. Finally, it is not a substitute for
institution-specific charters, which instead invites readers to draft or
update in light of the principles it brings together.

The report is conceived within the European normative landscape for AI in
research and the humanities. Its reference points include the EU Artificial
Intelligence Act (in force since August 2024, with final provisions taking
effect in August 2027), which applies to universities and requires a
baseline level of AI literacy for staff and students; the ALLEA European
Code of Conduct for Research Integrity (2023), the reference standard for
EU-funded research, built on the principles of reliability, honesty,
respect, and accountability; and the UNESCO (2021), which establishes
transparency, accountability, human oversight, and the protection of
cultural diversity as foundational principles.

These instruments are complemented by the seven requirements of the
European Framework for Trustworthy AI (2019), which orient the report’s
treatment of AI throughout: human agency and oversight; technical
robustness and safety; privacy and data governance aligned with FAIR/CARE;
transparency, traceability, and explicability; diversity,
non-discrimination, and fairness; societal and environmental well-being;
and accountability, including auditability and risk management. Further
reference is made to the EU Living Guidelines on the Responsible Use of
Generative AI in Research (2025) and to ENRIO procedural guidance on
research misconduct (2020).

The report provides a critical framework for evaluating AI tools, concrete
case studies, principles of data governance, and practical orientations for
teaching and assessment. It also supplies a shared vocabulary and set of
reference points useful for shaping institutional policy, while maintaining
that responsibility for AI-mediated research outputs ultimately remains
with human researchers and their institutions.

***
--
*Javier Marín-López*
Catedrático de Universidad, Área de Música
Director del Secretariado de Editorial Universitaria y Proyección de la
Cultura
www.javiermarinlopez.com
marin at ujaen.es
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*Universidad de Jaén*
Dpto. Didáctica de la Expresión Musical, Plástica y Corporal
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