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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">infojournal</journal-id><journal-title-group><journal-title xml:lang="ru">Информатика и образование</journal-title><trans-title-group xml:lang="en"><trans-title>Informatics and education</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">0234-0453</issn><issn pub-type="epub">2658-7769</issn><publisher><publisher-name>Publishing House Education and Informatics</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.32517/0234-0453-2026-41-3-49-61</article-id><article-id custom-type="elpub" pub-id-type="custom">infojournal-1508</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>ПЕДАГОГИЧЕСКИЙ ОПЫТ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>PEDAGOGICAL EXPERIENCE</subject></subj-group></article-categories><title-group><article-title>Развитие аспектов критического мышления у студентов посредством обучения промптинжинирингу</article-title><trans-title-group xml:lang="en"><trans-title>Fostering critical thinking skills in students through prompt engineering training</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0000-9989-9770</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Киселёва (Лавровская)</surname><given-names>В. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Kiselyova (Lavrovskaia)</surname><given-names>V. G.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Киселёва (Лавровская) Вера Григорьевна, соискатель ученой степени кандидата наук, Институт образования; ; ассистент кафедры телекоммуникаций, Институт радиоэлектроники и информатики</p><p>г. Москва</p></bio><bio xml:lang="en"><p>Vera G. Kiselyova (Lavrovskaia), PhD Candidate, Institute of Education; Assistant at the Department of Telecommunications, Institute of Radio Electronics and Informatics</p><p>Moscow</p></bio><email xlink:type="simple">vglavrovskaia@hse.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Национальный исследовательский университет «Высшая школа экономики»; МИРЭА — Российский технологический университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>National Research University Higher School of Economics (HSE University); MIREA — Russian Technological 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>17</day><month>08</month><year>2026</year></pub-date><volume>41</volume><issue>3</issue><fpage>49</fpage><lpage>61</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Киселёва (Лавровская) В.Г., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Киселёва (Лавровская) В.Г.</copyright-holder><copyright-holder xml:lang="en">Kiselyova (Lavrovskaia) V.G.</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://info.infojournal.ru/jour/article/view/1508">https://info.infojournal.ru/jour/article/view/1508</self-uri><abstract><p>Развитие генеративного искусственного интеллекта (ГенИИ) и его интеграция в индустриальную сферу влияют на спектр навыков, востребованных на рынке труда. Для подготовки востребованных специалистов системе высшего образования необходимо развивать у студентов как навыки взаимодействия с большими языковыми моделями (БЯМ), так и гибкие навыки, такие как критическое мышление, позволяющие ориентироваться в изменяющихся требованиях. Промпт-инжиниринг как процесс создания и модификации запросов к БЯМ становится важным инструментом в образовании, однако существующие работы, посвященные изучению потенциала промпт-инжиниринга для развития критического мышления, в основном носят теоретический характер. Целью проведенного эмпирического исследования стало выявление возможности развития отдельных аспектов критического мышления при обучении промпт-инжинирингу. В целевую выборку вошли 93 студента инженерного направления, которые коллективно выполняли задания по дисциплине «Правовое регулирование в области связи» с использованием инструментов ГенИИ. Тридцать восемь диалогов студентов с БЯМ были проанализированы с применением метода качественного контент-анализа. Выделены восемь паттернов взаимодействия, часть из которых связана с использованием техник промпт-инжиниринга. Наиболее популярным паттерном стало добавление информации из лекций или других источников, что указывает на стремление студентов к уточнению и улучшению ответов БЯМ. Проведенный анализ данных позволил выявить в промптах студентов проявление таких аспектов критического мышления, как анализ, сравнение и синтез информации. Сопоставление итогов двух этапов кодирования показало, что прослеживается связь трех из восьми паттернов, которые применяют студенты, с вышеупомянутыми аспектами критического мышления. Исследование демонстрирует, что обучение промпт-инжинирингу может стать перспективным образовательным инструментом для развития критического мышления у студентов при соблюдении определенных условий. Также результаты указывают на возможность использования анализа диалогов с БЯМ для оценки навыков студентов.</p></abstract><trans-abstract xml:lang="en"><p>The development of generative artificial intelligence (GenAI) and its integration into the industrial sector is influencing the range of skills in demand in the labor market. To prepare competitive professionals, higher education systems need to develop students’ skills in interacting with large language models (LLMs) as well as soft skills, such as critical thinking, which enable them to adapt to changing requirements. Prompt engineering, as the process of creating and modifying queries to LLMs, is becoming an important tool in education. However, existing research on the potential of prompt engineering for critical thinking development is primarily theoretical. The aim of this empirical study was to identify opportunities for developing specific aspects of critical thinking through prompt engineering training. The target sample included 93 engineering students who collaboratively completed tasks in the discipline “Legal regulation in the field of communications” using generative AI tools. Thirty-eight student dialogues with LLMs were analyzed using qualitative content analysis. Eight interaction patterns were identified, some of which were associated with the use of prompt engineering techniques. The most common pattern was the addition of information from lectures or other sources, indicating students’ efforts to refine and improve LLM responses. The data analysis revealed the manifestation of critical thinking aspects, such as analysis, comparison, and synthesis of information, in student prompts. A comparison of the results from two coding stages demonstrated a connection between three of the eight patterns and the aforementioned critical thinking aspects. The study suggests that prompt engineering training can become a promising tool for developing critical thinking in students, provided certain educational conditions are met. Additionally, the results indicate the potential of using LLM dialogue analysis to assess student skills.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>генеративный искусственный интеллект</kwd><kwd>ГенИИ</kwd><kwd>большие языковые модели</kwd><kwd>БЯМ</kwd><kwd>промпт-инжиниринг</kwd><kwd>паттерны взаимодействия с БЯМ</kwd><kwd>контент-анализ</kwd><kwd>критическое мышление</kwd><kwd>высшее образование</kwd><kwd>развитие навыков</kwd></kwd-group><kwd-group xml:lang="en"><kwd>generative artificial intelligence</kwd><kwd>GenAI</kwd><kwd>large language models</kwd><kwd>LLM</kwd><kwd>prompt engineering</kwd><kwd>LLM interaction patterns</kwd><kwd>content analysis</kwd><kwd>critical thinking</kwd><kwd>higher education</kwd><kwd>skills development</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Theophilou E., Koyutürk C., Yavari M., et al. 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