vovrВысшее образование в России (Vysshee obrazovanie v Rossii = Higher Education in Russia)Vysshee Obrazovanie v Rossii = Higher Education in Russia0869-36172072-0459Moscow Polytechnic University10.31992/0869-3617-2026-35-6-149-168vovr-6328Research ArticleСтатьиФакторы интеграции искусственного интеллекта в исследование: кейсы российских лабораторийFor Factors of AI Integration in Research: Case Study of Russian Laboratorieshttps://orcid.org/0009-0004-6088-057XСазоновА. А.SazonovA. A.

Сазонов Артём Алексеевич – младший научный сотрудник Научно-исследовательской лаборатории археологии и этнографии Сибири  

634050, г. Томск, ул. Ленина, д. 36

Artem A. Sazonov – Junior Researcher, Laboratory of Archaeology and Ethnography of Siberia  

36 Lenin ave., Tomsk, 634050

artemiy_sazonov@mail.ruhttps://orcid.org/0000-0003-1716-8849ПоповаЕ. В.PopovaE. V.

Попова Евгения Владимировна – канд. полит. наук, доцент, заведующая кафедрой антропологии и этнологии Факультета исторических и политических наук  

634050, г. Томск, ул. Ленина, д. 36

Evgeniya V. Popova – Cand.Sci. (Political Sciences), Associate Professor, Head of the Department of Anthropology and Ethnology, Faculty of Historical and Political Sciences  

36 Lenin ave., Tomsk, 634050

iam.e.popova@yandex.ruhttps://orcid.org/0000-0002-9809-082XМацепуроД. М.MatsepuroD. M.

Мацепуро Дарья Михайловна – старший научный сотрудник Научно-исследовательской лаборатории археологии и этнографии Сибири  

634050, г. Томск, ул. Ленина, д. 36

Daria M. Matsepuro – Cand.Sci. (History), Senior Researcher, Laboratory of Archaeology and Ethnography of Siberia  

36 Lenin ave., Tomsk, 634050

daria.matsepuro@mail.tsu.ruТомский государственный университетРоссияNational Research Tomsk State UniversityRussian Federation202624062026356149168Copyright © Сазонов А.А., Попова Е.В., Мацепуро Д.М., 20262026Сазонов А.А., Попова Е.В., Мацепуро Д.М.Sazonov A.A., Popova E.V., Matsepuro D.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vovr.elpub.ru/jour/article/view/6328

Для понимания факторов успешной интеграции технологий искусственного интеллекта (ИИ) как исследовательского метода в деятельность лаборатории необходимо обратиться к существующему опыту научных коллективов, проанализировать ключевые аспекты их работы и опыт внедрения искусственного интеллекта, успешный или нет. Цель настоящего исследования состоит в том, чтобы выявить и систематизировать ключевые переменные, определяющие возможность включения искусственного интеллекта в работу научных коллективов. Для этого мы используем подход множественного кейсстади. В центре внимания данного исследования четыре исследовательских коллектива сибирских научных и образовательных организаций. Научные группы работают или работали над проектами в разных предметных областях: физике, истории, медицине.

Мы выявили три взаимосвязанных условия успешного внедрения искусственного интеллекта. Во-первых, возможность гармонизации исследовательской задачи и формата данных – исследователи начинали активно интегрировать ИИ в свою работу, когда традиционные методы становились технически неэффективными. Во-вторых, формирование коллаборации со специалистами в области работы с данными и программистами, которая во многом зависела от активной организационной политики, позволяя приобрести необходимые компетенции. И, как следствие, двусторонняя коммуникация между исследователями и специалистами по ИИ позволила закрепить опыт применения технологии для действующего и будущих проектов. Таким образом, внедрение ИИ оказывается не столько линейным, технологически обусловленным процессом, сколько согласованием исследовательской задачи, имеющихся ресурсов, компетенций, коммуникативных навыков, организационной культуры и дисциплинарных норм валидации полученного знания.

To understand the factors of successful integration of artificial intelligence into research process, it is necessary to examine the existing experience of scientific collectives, analyze key aspects of their work and the experience of implementing artificial intelligence (AI), whether successful or not. The purpose of this study is to identify the key factors that determine the success of artificial intelligence integration into the research activities of scientific collectives.

To achieve the research objectives, we employ a multiple case study approach and conduct a series of semi-structured in-depth interviews. The focus of this study is four research collectives from Siberian scientific institutions. The scientific groups have been working or have worked on projects from different disciplines: physics, history, and medicine.

We identified three interconnected conditions for successful artificial intelligence implementation. First, the possibility of harmonizing the research task and data format. Active integration of AI into the work of research collectives started when traditional methods had become inefficient from technological standpoint. Second, active organizational policy allowed research collectives to establish collaborations with programmers, acquiring necessary competencies. Third, two-way communication between researchers and AI specialists enabled consolidation of experience in applying the technology, reducing costs, and integrating AI specialists into the scientific group. Thus, the implementation of AI proves to be not so much a linear, technologically determined process, but rather a coordination of the research task, available resources, competencies, organizational culture, and disciplinary norms for validating acquired knowledge.

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The authors declare that there are no conflicts of interest present.