IntroductionAs generative and chat-based artificial intelligence (AI) systems evolve into communication partners, students' attitudes toward these machine interlocutors have become a central question in communication research. These attitudes are not culturally neutral; they are shaped by the media ecology, linguistic repertoire, and social norms of the communication environment. Drawing on a humanmachine communication framework, this study reconceptualizes AI literacy as communicative competence with machine interlocutors and attitudes toward AI as culturally mediated evaluative orientations. The aim was to map their conditional architecture in the Russian cultural-communicative context.MethodsIn a cross-sectional design, 668 undergraduate students from three Russian universities completed the Russian-adapted Meta Artificial Intelligence Literacy Scale and the General Attitudes towards Artificial Intelligence Scale. A standardized partial correlation network was estimated. Centrality, bridge centrality, network invariance, and community structure were examined.ResultsThe strongest bridge was found between practical interaction with machines (Apply AI) and positive attitude. Exploratory network analysis placed Apply AI within the attitude cluster. Critical evaluation was located at the structural center of the network. Negative attitude remained peripheral. Persuasion and emotion regulation competencies merged into a single ESEM dimension. The network remained invariant across gender and frequency of use.DiscussionThis suggests that machine interaction and its evaluation form an integrated communicative-relational domain. A linguistic-cultural reading of this pattern is offered as a post hoc interpretation. The findings extend the human-machine communication framework to Russian higher education, a setting underrepresented in AI literacy research. They also support the reconceptualization of AI literacy as a communicative-cultural construct.
The research aims to determine the extent to which ultrasound tongue imaging (UTI) can be utilized as a method for confirming and refining the interpretation of formant data when comparing different pronunciation variants of standard literary Russian. The article examines the possibilities of the combined application of acoustic analysis and ultrasound visualization using Moscow and Southern Russian phono-variants of the vowels /a/ and /o/ in stressed and first pre-stressed positions. Vowel realization areas within the F1-F2 plane, alongside tongue surface contour tracing data, are presented, which allow for the correlation of formant characteristics with specific articulatory features. The scientific novelty of the study lies in the fact that ultrasound imaging is considered not in isolation but as a tool for the verification and refinement of acoustic interpretations of formant shifts during the analysis of regionally determined vocalic variation. The results establish that in the stressed position, differences between the Moscow and Southern Russian phono-variants are minimal and are confirmed by the similarity in articulatory configuration. Conversely, in the first pre-stressed position, differences are more pronounced: the Moscow variant is characterized by a more compact, more fronted, and higher realization, while the Southern Russian variant demonstrates greater dispersion and a lower, more retracted type of articulation. It is shown that UTI data generally confirm the interpretation of formant data and expand the possibilities for describing reduction, coarticulation, and variation in Russian vocalism.
Статья посвящена разработке методики оценки крайней необходимости в уголовном праве че- рез системный анализ альтернатив поведения и сопоставление охраняемых благ. Целью исследования является преодоление декларативности требования о «меньшем вреде», закрепленного в статье 39 Уголовного кодекса Российской Федерации, посредством создания операциональных критериев сравнения. Методологическую основу составляют формально-юридический метод, аксиологический анализ и кейс-метод. Сформулированы критерии сравнения причиненного и предотвращенного вреда, проведено разграничение крайней необходи- мости и обоснованного риска, предложена авторская кейс-методика оценки. Научная новизна состоит в разра- ботке «треугольника оценки», интегрирующего три параметра: ценность охраняемого блага, вероятность при- чинения ущерба и наличие альтернатив поведения. Обосновано, что ключевым элементом квалификации край- ней необходимости выступает доказанная безальтернативность или рационально ограниченная альтернатива действий, а не только итоговое соотношение причиненного и предотвращенного вреда. Разработана матрица аргументации для стороны защиты и обвинения, а также критерии «неустранимости опасности». The article is devoted to the development of a methodology for assessing extreme necessity in criminal law through a systematic analysis of behavioral alternatives and comparison of protected interests. The aim of the study is to overcome the declarative nature of the «lesser harm» requirement enshrined in Article 39 of the Criminal Code of the Russian Federation by creating operational comparison criteria. Criteria for comparing inflicted and prevented harm have been formulated, a distinction between extreme necessity and justified risk has been drawn, and an original case-based assessment methodology has been proposed. The scientific novelty consists in the development of an «assessment triangle» integrating three parameters: the value of the protected interest, the probability of damage, and the availability of behavioral alternatives.
The aim of the study is to develop and test a corpus-based model for reconstructing the cognitive-discursive matrix of teacher professional identity. Professional identity is interpreted as a discursively represented configuration of linguistic, conceptual, terminological, pragma-axiological and metaphorical parameters. The empirical basis of the study is a specialised corpus of English-language professional pedagogical discourse comprising more than 2,700 pages and over 2 million word tokens. The corpus includes written and oral materials: monographs, textbooks, research and popular-science articles, lectures, and professional educational texts. The research methodology combines digital text preprocessing, frequency analysis, reconstruction of dominant concepts, analysis of terminological density, pragma-axiological modelling, and identification of conceptual metaphors. The study established that the core of the teacher’s cognitive-discursive matrix is formed by six dominant concepts: “education”, “language”, “culture”, “identity”, “research”, and “method”. An inverse functional relationship was identified between terminological density and pragma-axiological load: “method” demonstrates the highest degree of professional specialisation and the lowest axiological load, whereas “identity” and “culture” display the strongest value-related charge. The analysis of conceptual metaphors revealed 13 dominant models, among which “education is a journey”, “learning is growth”, “teacher is a guide”, and “teaching is growth” constitute the core models. The study concludes that corpus-based modelling makes it possible to transfer teacher professional identity from the domain of interpretative description to the plane of reproducible analysis of discourse parameters, while the cognitive-discursive matrix serves as an effective operational tool for reconstructing teacher professional subjectivity within the digital linguistic paradigm.
This systematic review investigates the current state of artificial intelligence (AI) integration in pre-service teacher (PST) education, with an emphasis on PSTs’ perspectives, attitudes, knowledge levels, and AI-related educational experiences. The review intends to uncover the characteristics that influence PSTs’ intents to employ AI technology, as well as the success of AI training programs. A thorough search of academic databases turned up 33 research published between 2021 and 2024, which were examined using a theme framework. The findings show that PSTs have both positive and negative attitudes about AI integration, with initial AI knowledge and skills being restricted but improving with targeted training and hands-on experiences. Perceived utility, ease of use, social impact, and self-efficacy have all been proven to influence PSTs’ propensity to employ AI. The review also emphasizes PSTs’ favorable experiences using AI-based instruction, such as lesson planning, collaborative learning, and feedback/evaluation. However, issues and ethical concerns regarding data privacy, academic honesty, fairness, and the possible harmful impact on student learning were highlighted. The review recommends that teacher education institutes prioritize AI literacy development, address PSTs’ concerns, and incorporate ethical considerations into AI courses. The findings add to the expanding body of literature on AI integration in education, providing useful insights for defining teacher education practice and policy in the AI era.
