Artificial intelligence in the methodology of audiovisual art and media production research
DOI:
https://doi.org/10.34142/27091805.2026.7.02.25Keywords:
research methodology, artificial intelligence, audiovisual art and media production, computer vision, distant viewing, master's qualification workAbstract
The purpose of the study: the study substantiates the methodological limits of using artificial intelligence (AI) in scholarly research on audiovisual art and media production, distinguishing the functions in which AI extends the art scholar's toolkit from those in which its use undermines validity or violates academic integrity.
Methods: a systematic review of scholarly sources (2019–2026) in digital humanities, computational study of visual culture, and the ethics of scholarly communication; comparative analysis of editorial AI policies; typologisation of AI functions; modelling of a procedural protocol. The empirical basis is the teaching of the course "Methodology of Scientific Research in the Media Industry" to master's students in Audiovisual Art and Media Production.
Scientific novelty: general principles of responsible AI use are transferred for the first time to the specific conditions of art-historical inquiry into the audiovisual work, whose object is neither a text nor a homogeneous numerical array but a polyphonic temporal structure; a tiered model of the admissibility of AI assistance, aligned with the logic of a master's qualification work in a creative speciality, is proposed.
Results: AI occupies three positions in research such as instrument, object, and environment of artistic practice – and conflating them is the principal source of methodological error. Computer vision and the distant viewing methodology extend formal and stylistic analysis to corpus scale yet do not replace interpretation. A typology of AI functions is proposed, with three modes: permitted without disclosure, permitted with mandatory disclosure, and unacceptable. Seven epistemological risks and four validity criteria are identified, and a protocol for integrating AI into master's research is developed.
Conclusions: the most productive form of computational involvement is a two-stage design: computational selection of corpus anomalies followed by their explanation through close reading. Operations whose results can be independently verified are acceptable subject to disclosure; interpretation, scientific novelty, and conclusions cannot be delegated without loss of authorship.
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References
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COPE Council. (2023). COPE position statement: Authorship and AI tools. https://doi.org/10.24318/cCVRZBms [in English]
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Esposito, E. (2022). Artificial communication: How algorithms produce social intelligence. The MIT Press. [in English]
Huang, J., Hitchen, G., & Dogan, S. (2025). Generative AI in the screen and live performance industries: A conceptual framework and prospects for future research. Convergence. https://doi.org/10.1177/13548565251383409 [in English]
ICMJE. (n.d.). Defining the role of authors and contributors. International Committee of Medical Journal Editors. https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html [in English]
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UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO. [in English]
Zhang, R., Yu, B., Min, J., Xin, Y., Wei, Z., Shi, J. N., ... Rao, A. (2025). Generative AI for film creation: A survey of recent advances. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. https://doi.org/10.48550/arXiv.2504.08296 [in English]
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