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| 1 | The article examines the main directions of work in the area for academic work of students and young scientists of universities. We studied the organization of scientific-research work of students at the Tomsk State Pedagogical University and the National Research Tomsk State Polytechnic University. It provides recommendations for student motivation to perform research work. Keywords: scientific research, scientific creativity, the learning process, conferences, competitions, motivation of students | 2315 | ||||
| 2 | For increase of the status of health and safety bases subject in educational institutions, revealing professional competence, supports and encouragements of talented teachers, increases of their pedagogical skill in Tomsk region the first regional competition of professional skill of health and safety bases teachers «Best teacher of health and safety bases» was held. In article the purposes and competition problems reveal, the organization of its carrying out is described. Keywords: Creative development, competition, pedagogical skill, high school, professional work of the teacher, health and safety, the pedagogical idea, open lesson, practical skills, professional competences | 2039 | ||||
| 3 | The article states that the integration of artificial intelligence (AI) into school education faces a contradiction between its transformative potential and social barriers linked to digital competence, the professional identity of teachers, and ethical risks. Despite growing interest in AI tools, their implementation is constrained by the inertia of traditional pedagogical practices and the disconnect between technological determinism and the social construction of innovation. The study aims to identify patterns in the perception of AI by students and teachers, assess the impact of digital literacy, age, and professional experience on readiness to adopt AI technologies. The empirical basis includes data from online surveys of 169 9th–11th grade students and 40 teachers from schools in the Tomsk region. The theoretical framework integrates and evaluates the SCOT, SAMR, TPACK, and Human-AI Collaboration Theory models to analyze social and technological factors influencing AI adoption. Survey results indicate that 57 % of students support AI education, while 27.2 % exhibit technophobia, associated with low algorithmic literacy and fears of AI errors. Among teachers, 65% use digital technologies, but only 37 % employ AI daily. Key risks identified by respondents include the diminishing role of teachers (38.5%), privacy threats (75 % of girls), and passive learning due to automation (33.7 %). Correlations were found between teachers’ age (younger educators more enthusiastically adopt AI), students’ technical orientation (STEM interests enhance AI acceptance), and school digitalization levels. The study confirms that successful AI integration requires a combination of technological infrastructure, ethical standards, and targeted professional development programs. Recommendations include phased AI adoption (from automation to personalization), project-based formats for technically oriented students, and dialogue among all educational stakeholders. The findings contribute to developing strategies for harmonizing AI solutions while preserving human agency in pedagogy. Keywords: artificial intelligence in education, social construction of technology (SCOT), digital competence of teachers, personalized learning, digital divide, human-AI collaboration | 1421 | ||||
| 4 | This article addresses the problem of developing an integrative theoretical framework for the analysis and design of artificial intelligence (AI) interventions in educational environments. The aim of the study is to develop an integrative theoretical framework that enables the description and design of pedagogically meaningful AI interventions in education. Based on a systematic secondary analysis of literature reviews published between 2019 and 2026 on AI in education, cognitive offloading, learner agency, and AI ethics, as well as a conceptual comparison of existing theoretical frameworks, the article examines the “cognitive paradox” of AI interventions: technologies that reduce working memory load can simultaneously lead to cognitive offloading and the accumulation of “cognitive debt” – a hidden gap between demonstrated academic performance and the learner’s actual cognitive infrastructure. A two-axis “cognitive offloading – agency” model is proposed, distinguishing four typical configurations of learner – AI agent interaction. Among these configurations, the one termed “extended cognition” is identified as the most promising for the development of self-regulation. Furthermore, the article demonstrates how the SAMR (Substitution, Augmentation, Modification, Redefinition), TPACK (Technological Pedagogical Content Knowledge), JTBD (Jobs to Be Done), and SCOT (Social Construction of Technology) frameworks can be refined by incorporating the dimensions of cognitive offloading and agency. This refinement transforms them from descriptive taxonomies into pedagogical design tools that account for the risks of “cognitive erosion”. By integrating cognitive considerations with established technology integration models, the proposed framework offers a more nuanced understanding of how AI agents reshape learning processes. Finally, the article identifies research gaps and outlines prospects for empirical testing of the proposed framework in both Russian and international educational contexts, emphasizing the need for longitudinal and mixed-method studies that track the long-term effects of AI use on learner autonomy and cognitive skills. Keywords: artificial intelligence in education, chatbots, cognitive load theory, self-regulated learning, Substitution – Augmentation – Modification – Redefinition (SAMR), Technological Pedagogical Content Knowledge, Social Construction of Technology | 70 | ||||