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Тревожность относительно искусственного интеллекта в образовательной среде: психометрическая адаптация шкалы на выборке российских студентов
Высшее образование в России. 2026. Т. 35. № 6. С. 102–126.
Today, there is a growing anxiety about artificial intelligence in the educational space, which greatly affects the willingness to make technological decisions and plan for one’s professional future. In empirical studies, AI anxiety is defined as a complex phenomenon including a range of negative emotional responses that shapes in people due to development and potential impact of artificial intelligence on various spheres of life. Given the impact of AI anxiety on learning, career choices, participation in the digital economy, and mental well-being, there is a need for its holistic conceptual description and reliable diagnosis. The aim of the study is to present a reliable and valid Russian-language tool for diagnosing AI anxiety in students. The adopted basis and modified for a student sample (N = 521) “Artificial Intelligence Anxiety Scale” (M.H. Alkhaibani et al.) showed an acceptable level of the external and internal validity as well as reliability on internal consistency. The factor analysis confirmed the five-factor structure of the questionnaire: concerns related to AI accuracy and reliability; concerns related to academic integrity (plagiarism); and concerns stemming from regulatory uncertainty (guidelines), fears of loss of learning and cognitive skills, as well as a decrease in motivation. External validity is confirmed by the correlations with neuroticism and conscientiousness as personality traits, image of the future, attitude towards artificial intelligence, and some socio-demographic characteristics. Adapted and modified psychodiagnostic tool for studying AI anxiety opens up opportunities for research on a Russian-speaking student sample.
Keywords: uncertaintyсамоэффективностьучебная мотивацияRussian studentsроссийские студентынеопределённостьeducational motivationself-efficacyacademic integrityакадемическая добросовестность artificial intelligenceискусственный интеллект (ИИ)psychometric adaptationAI anxietyAI anxiety scaleИИ-тревожностьшкала ИИ-тревожностипсихометрическая адаптация
Medushevsky A. N., Вопросы теоретической экономики 2026 № 2 С. 146–164
It is generally accepted that the basis of social and legal stability in any society is a consensus on vir tue — the fundamental values of proper behavior, ways of maintaining and reproducing them. And, conversely, the destruction of this consensus is a sign of the loss of stability and divergence of positions of social ...
Added: August 26, 2026
Medushevsky A. N., Сравнительное конституционное обозрение 2025 Т. 34 № 4 С. 135–166
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Voronina Y., Kuryan M., Педагогика и психология образования 2026 № 2 С. 84–98
Teaching argumentation in foreign language classes is a promising approach, as it enhances students' critical thinking and improves the quality of their academic work. Despite the rather widespread use of explicit methods for developing argumentation skills, the implicit approach remains underexplored. This paper proposes a system for organizing implicit argumentation instruction in English based on ...
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Учебный курс предназначен для студентов направлений журналистики и медиакоммуникаций, владеющих английским языком на уровне B2–C1. Курс отражает ключевые вызовы современной цифровой медиасреды: использование искусственного интеллекта, борьбу с дезинформацией, развитие медиаграмотности, влияние социальных сетей, мобильную журналистику, цифровую идентичность и перспективы развития медиа. Обучение построено на аутентичных материалах и практико-ориентированных заданиях, моделирующих реальные профессиональные ситуации. Выполнение интерактивных ...
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Diachkova M., Lelik V., Dorofeeva S. et al., Language Resources and Evaluation 2026 Vol. 60 Article 68
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The current exploratory study aims to reveal identity gaps and interpersonal communication strategies used by Russians to overcome social identity crises. Based on previous research, we assume that many Russians who live both in Russia and abroad face ethnic and national identity threats and experience different social identity crises due to recent geopolitical events. This ...
Added: August 6, 2026
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This volume explores how generative artificial intelligence is reshaping universities worldwide. Rather than focusing only on the technology, it shows how Artificial Intelligence (AI) is changing how students learn, teachers teach, and institutions operate, while also being shaped by cultural values, educational traditions, and social expectations.
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Added: August 6, 2026
Kanaev E., Southeast Asia: Actual Problems of Development 2026 Vol. 2 No. 2(71) P. 11–22
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Klementiev A., Kasatkina A., Legal Issues in the Digital Age 2026 Vol. 7 No. 2 P. 40–60
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The integration of artificial intelligence (AI) into information technologies is reshaping competency requirements for IT specialists. However, AI competency penetration into the early phases of the Software Development Life Cycle (SDLC) – requirements analysis and planning – remains underexplored. Existing studies either rely on static competency models lacking empirical validation or analyze the labor market ...
Added: July 5, 2026
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Added: June 26, 2026
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Added: June 23, 2026
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The roundtable was organized by the journal “Voprosy Filosofii” and representatives of the research project No. 25-28-00707, “Social Programming as a Problem of Network ...
Added: June 18, 2026
Romanova T. V., Бинштейн М. М., Медиалингвистика 2026 Т. 13 № 1 С. 28–50
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Added: June 14, 2026
Barkhatova L., Социологические исследования 2026 № 5 С. 71–83
The article conceptualizes the methodological potential of hyperlink network analysis as a tool of empirical reconstruction of social interaction through digital traces. Using the partnership network of Russia’s artificial intelligence (AI) ecosystem as a case, the paper presents an original analytical framework for translating technical hyperlink data into sociological categories. The study describes a full ...
Added: June 12, 2026
Butorova A., Bobakov V., Sergeev A. et al., European Physical Journal: Special Topics 2026 P. 1–19
This paper presents a review of artificial intelligence (AI) methods for failure prediction in data center cooling systems, with a focus on the integration of digital twins (DTs), physics-informed learning, and graph-based models. Positioned within complex network science, this review addresses a limitation of conventional graph approaches—their reliance on pairwise connectivity—whereas real-world failures often arise ...
Added: June 10, 2026