vovrВысшее образование в России (Vysshee obrazovanie v Rossii = Higher Education in Russia)Vysshee Obrazovanie v Rossii = Higher Education in Russia0869-36172072-0459Moscow Polytechnic University10.31992/0869-3617-2026-35-2-53-73vovr-6010Research ArticleСтатьиПромпт-инжиниринг как ключевая компетенция в образовании: сущность, особенности и подходы к оцениваниюPrompt Engineering as a Key Competence in Education: Concept, Characteristics, and Assessment Approacheshttps://orcid.org/0000-0003-1245-2680ДавлатоваМ. А.DavlatovaM. A.

Мадина Асатуллоевна Давлатова, канд. наук об образовании, доцент, зам. директора Департамента, эксперт

Департамент образовательных программ; Институт образования; Центр поддержки цифрового обучения

101000; Потаповский пер., д. 16, стр. 10; Москва

Madina A. Davlatova, Cand.Sci. (Education), Associate Professor, Deputy Director of the Department, Expert

Department of Educational Programmes; Institute of Education; Digital Education Support Centre

101000; 16 Potapovsky lane, bld. 10; Moscow

mdavlatova@hse.ruhttps://orcid.org/0009-0005-0956-2995СперанскаяМ. В.SperanskaiaM. V.

Марина Викторовна Сперанская, главный специалист Центра

Высшая школа менеджмента; Центр преподавательского мастерства в бизнес-образовании

199004; Волховский пер., д. 3; Санкт-Петербург

Marina V. Speranskaia, Chief Specialist of the Center

Graduate School of Management; Center for Teaching Excellence in Business Education

199004; 3 Volkhovskiy lane; St. Petersburg

mv.speranskaya@yandex.ruНациональный исследовательский университет «Высшая школа экономики»РоссияNational Research University Higher School of EconomicsRussian FederationСанкт-Петербургский государственный университетРоссияSaint Petersburg State UniversityRussian Federation2026040320263525373Copyright © Давлатова М.А., Сперанская М.В., 20262026Давлатова М.А., Сперанская М.В.Davlatova M.A., Speranskaia M.V.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/6010

   В условиях активного развития генеративного искусственного интеллекта (ГенИИ) промпт-инжиниринг становится ключевой компетенцией для эффективного взаимодействия с большими языковыми моделями в образовательном контексте. Однако отсутствие единого понимания сущности, структуры и инструментов оценивания этой компетенции затрудняет его интеграцию в образовательный процесс.

   Цель исследования заключается в том, чтобы систематизировать знания о промпт-инжиниринге как о ключевой компетенции в образовании, определить его особенности и подходы к оцениванию.

   В работе проведён скопинговый обзор (scoping review) более 60 источников, включая рецензируемые научные статьи, материалы международных конференций, документацию технологических компаний и т. д. Поиск осуществлялся в базах данных Google Scholar, ERIC, КиберЛенинка и др. за период 2020–2025 гг. В результате исследования было выявлено, что промпт-инжиниринг является междисциплинарной компетенцией, интегрирующей знания, умения и установки для эффективного взаимодействия с ГенИИ. В работе описаны техники промпт-инжиниринга (Zero-shot, few-shot, Chain-of-Thought, Tree-of-Thought, ReAct, Self-Consistency и др.) и систематизированы по двум осям: по уровню предварительной информации и по способу логического построения промпта. Проанализирована эволюция промпт-инжиниринга на основе зарубежных и российских исследований и практик. Предложена операциональная модель компетенции, основанная на таксономии Блума – Андерсона, с разбивкой по шести когнитивным уровням (запоминание, понимание, применение, анализ, оценка и создание) и трём измерениям (знания, умения, установки).

   In the context of the rapid development of Generative Artificial Intelligence (GenAI), prompt engineering is becoming a key competence for effective interaction with large language models in educational settings. However, the lack of a unified understanding of its nature, structure, and assessment tools complicates its integration into educational practice.

   The aim of this study is to systematize knowledge about prompt engineering as a key competence in education, identify its distinctive features, and explore approaches to its assessment.

   The study is based on a scoping review of more than 60 sources, including peer-reviewed journal articles, international conference proceedings, technology company documentation, and other materials. The search was conducted in databases such as Google Scholar, ERIC, СyberLeninka, and others for the period 2020–2025. The findings reveal that prompt engineering is an interdisciplinary competence that integrates knowledge, skills, and attitudes required for effective interaction with GenAI. The paper describes key prompt engineering techniques (Zero-shot, Few-shot, Chain-of-Thought, Tree-of-Thought, ReAct, Self-Consistency, etc.) and systematizes them along two dimensions: the level of prior information and the type of logical structure used in prompt construction. The evolution of prompt engineering is analyzed based on international and Russian research and practices. An operational model of the competence is proposed, grounded in the Bloom–Anderson taxonomy, with differentiation across six cognitive levels (remember, understand, apply, analyze, evaluate, and create) and three dimen-sions (knowledge, skills, attitudes).

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