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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vovr</journal-id><journal-title-group><journal-title xml:lang="ru">Высшее образование в России  (Vysshee obrazovanie v Rossii = Higher Education in Russia)</journal-title><trans-title-group xml:lang="en"><trans-title>Vysshee Obrazovanie v Rossii  = Higher Education in Russia</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">0869-3617</issn><issn pub-type="epub">2072-0459</issn><publisher><publisher-name>Moscow Polytechnic University</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.31992/0869-3617-2026-35-7-76-104</article-id><article-id custom-type="elpub" pub-id-type="custom">vovr-6433</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>Статьи</subject></subj-group></article-categories><title-group><article-title>Карьерные траектории выпускников образовательных программ в сфере искусственного интеллекта: результаты качественного исследования регионального вуза</article-title><trans-title-group xml:lang="en"><trans-title>Career Trajectories of Graduates of Educational Programs in the Field of Artificial Intelligence: Results of a Qualitative Study of a Regional University</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2884-8110</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Аверьянов</surname><given-names>А. О.</given-names></name><name name-style="western" xml:lang="en"><surname>Averyanov</surname><given-names>A. O.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Аверьянов Александр Олегович – канд. эконом. наук, старший научный сотрудник, начальник отдела прогнозирования потребности экономики в кадрах</p><p>Researcher ID: GLT-8674-2022</p><p>185910, Республика Карелия, г. Петрозаводск, пр-кт Ленина, д. 31</p></bio><bio xml:lang="en"><p>Aleksandr O. Averyanov – Cand.Sci. (Economic Sciences), Senior Researcher, Head of the Department for Forecasting the Economy’s Need for Personnel</p><p>Researcher ID: GLT-8674-2022</p><p>31 Lenin ave., Petrozavodsk, Republic of Karelia, 185910</p></bio><email xlink:type="simple">aver@petrsu.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1990-9826</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Симакова</surname><given-names>А. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Simakova</surname><given-names>A. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Симакова Анна Васильевна – канд. социол. наук, старший научный сотрудник</p><p>Researcher ID: K-3038-2018</p><p>185910, Республика Карелия, г. Петрозаводск, пр-кт Ленина, д. 31</p></bio><bio xml:lang="en"><p>Anna V. Simakova – Cand.Sci. (Sociological Sciences), Senior Researcher</p><p>31 Lenin ave., Petrozavodsk, Republic of Karelia, 185910</p></bio><email xlink:type="simple">simakova@petrsu.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-5070-0273</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Степусь</surname><given-names>И. С.</given-names></name><name name-style="western" xml:lang="en"><surname>Stepus</surname><given-names>I. S.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Степусь Ирина Сергеевна – канд. эконом. наук, старший научный сотрудник, заместитель директора по НИР</p><p>Researcher ID: H-1098-2016</p><p>185910, Республика Карелия, г. Петрозаводск, пр-кт Ленина, д. 31</p></bio><bio xml:lang="en"><p>Irina S. Stepus – Cand.Sci. (Economic Sciences), Senior Researcher, Deputy Director for Research</p><p>Researcher ID: H-1098-2016</p><p>31 Lenin ave., Petrozavodsk, Republic of Karelia, 185910</p></bio><email xlink:type="simple">stepus@petrsu.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Центр бюджетного мониторинга Петрозаводского государственного университета</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Center for Budget Monitoring of Petrozavodsk State University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>05</day><month>08</month><year>2026</year></pub-date><volume>35</volume><issue>7</issue><fpage>76</fpage><lpage>104</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Аверьянов А.О., Симакова А.В., Степусь И.С., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Аверьянов А.О., Симакова А.В., Степусь И.С.</copyright-holder><copyright-holder xml:lang="en">Averyanov A.O., Simakova A.V., Stepus I.S.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vovr.elpub.ru/jour/article/view/6433">https://vovr.elpub.ru/jour/article/view/6433</self-uri><abstract><p>В статье рассмотрена подготовка вузами специалистов в сфере искусственного интеллекта (ИИ) на основе анализа начала карьерных траекторий выпускников специализированных образовательных программ магистратуры по профилю «искусственный интеллект». Актуальность исследования обусловлена приоритетным значением кадрового обеспечения развития ИИ-технологий. Научная новизна исследования определяется его объектом, на основе метода глубинных интервью авторами проведён анализ эффективности подготовки кадров для сферы ИИ. Цель исследования – определить основные барьеры трудоустройства выпускников ОПОП ИИ по профилю полученного образования на примере регионального вуза. Эмпирическая база исследования – транскрипты интервью с выпускниками образовательных программ магистратуры по профилю «искусственный интеллект» 2024 и 2025 гг. выпуска (N = 25) и транскрипты фокус-групп со студентами бакалавриата (N = 2) и магистратуры (N = 2) этого же профиля. Дополнительно рассмотрены учебные планы и рабочие программы дисциплин анализируемых образовательных программ. В ходе исследования выявлено, что трудоустроенными по профилю полученного образования являются 7 из 25 обследованных выпускников (28%). Основное влияние на это оказывают: отсутствие релевантного опыта и недостаток практических навыков; трудоустройство в интересующей области ИТ на момент поступления в магистратуру; отсутствие изначального интереса к работе с ИИ-технологиями. Более детальный анализ перечисленных факторов выявил, что половина выпускников рассматривали поступление в магистратуру как продолжение получения бакалаврского образования, а идентифицировали образовательную программу с ИИ-технологиями лишь 15 из 25 выпускников. Также респонденты отметили недостаточную практикоориентированность образовательных программ. Среди общеизвестных факторов, способствующих трудоустройству по профилю полученного образования, таких как получение практического опыта работы с ИИ в процессе обучения, выявлено значимое влияние на трудоустройство в сферу ИИ профессиональной самоидентификации выпускника как исследователя. Практическая значимость работы заключается в формировании на основе полученных результатов рекомендаций, способствующих модернизации существующих практик отечественного высшего образования в сфере ИИ.</p></abstract><trans-abstract xml:lang="en"><p>This article examines the training of specialists in the field of artificial intelligence (AI) by universities based on an analysis of the initial career trajectories of graduates of specialized master’s educational programs in the field of artificial intelligence. The relevance of the study is determined by the priority importance of human resources for the development of AI technologies. The scientific novelty of the study is determined by its object: using the method of in-depth interviews, the authors analyzed the effectiveness of training personnel for the field of AI. The objective of the study is to identify the main barriers to employment of graduates of the specialized educational program in AI in the field of their education using the example of a regional university. The empirical base of the study is transcripts of interviews with graduates of master’s educational programs in the field of artificial intelligence, graduating in 2024 and 2025 (N = 25) and transcripts of focus groups with undergraduate (N = 2) and master’s (N = 2) students of the same program. In addition, the curricula and syllabi of the disciplines of the analyzed educational programs are considered. The study revealed that 7 out of 25 graduates (28%) are employed in their field of study. This is primarily influenced by a lack of relevant experience and practical skills; employment in the IT field of interest at the time of enrollment in master’s program; and a lack of initial interest in working with AI technologies. A more detailed analysis of these factors revealed that half of the graduates viewed enrollment in a master’s program as a continuation of their bachelor’s degree, while only 15 out of 25 identified an educational program with AI technologies. Respondents also noted the educational programs’ insufficient practical focus. Among well-known factors contributing to employment in their field of study, such as gaining practical experience with AI during training, a graduate’s professional identity as a researcher significantly influences employment in the AI field. The practical significance of the work lies in the formation of recommendations based on the obtained results that contribute to the modernization of existing practices in domestic higher education in the field of AI.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>развитие инноваций</kwd><kwd>искусственный интеллект</kwd><kwd>квалифицированные кадры</kwd><kwd>ИТ-образование</kwd><kwd>трудоустройство выпускников</kwd><kwd>карьерные траектории</kwd><kwd>профессиональная идентичность</kwd><kwd>качественное исследование</kwd><kwd>глубинное интервью</kwd><kwd>фокус-группа</kwd></kwd-group><kwd-group xml:lang="en"><kwd>innovation development</kwd><kwd>artificial intelligence</kwd><kwd>qualified personnel</kwd><kwd>IT education</kwd><kwd>university graduate employment</kwd><kwd>career trajectories</kwd><kwd>professional identity</kwd><kwd>qualitative research</kwd><kwd>indepth interviews</kwd><kwd>focus group</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Исследование выполнено за счёт гранта Российского научного фонда, проект № 25-28-00827 «Кадровое обеспечение инновационного процесса на примере сферы искусственного интеллекта».</funding-statement><funding-statement xml:lang="en">The research was carried out at the expense of a grant from the Russian Science Foundation, project No. 25-28-00827 “Staffing the innovation process using the example of artificial intelligence”.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Phelps E.S. 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