Accurate measurement of energy expenditure during adolescent physical activities is fundamental for evaluating exercise intensity, scientifically developing exercise prescriptions, and monitoring physical health status. With increasing national attention to adolescent physical fitness, the demand for scientific monitoring of students' energy expenditure in physical activities has become increasingly urgent. This paper systematically reviews current mainstream energy expenditure measurement methods, including the doubly labeled water method, indirect calorimetry, heart rate monitoring, motion sensor methods, and physical activity questionnaires, analyzing the principles, characteristics, and limitations of each method when applied to adolescent physical activities. On this basis, the paper focuses on emerging technological directions for energy expenditure measurement, particularly non-contact measurement methods based on computer vision and deep learning-based energy expenditure prediction models. The review reveals that traditional measurement methods generally face challenges in adolescent physical education settings, including high cost, cumbersome operation, and interference from wearable devices with normal physical movement, whereas non-contact measurement methods show promise in overcoming these limitations. Finally, this paper provides an outlook on future research directions for energy expenditure measurement methods in adolescent physical activities.

The Master of Agricultural Management plays an important role in cultivating high-level "agriculture, rural areas, and farmers" (san nong) management professionals for the implementation of the Rural Revitalization Strategy. However, the traditional competency-oriented model of talent cultivation for this degree can no longer meet the needs arising from the Rural Revitalization Strategy. Based on an analysis of the influencing factors and dimensions of competency requirements for agricultural management positions under the context of rural revitalization, this study adopts both interview and questionnaire survey methods to identify the core competency requirements for the Master of Agricultural Management. Accordingly, a core competency indicator system is constructed, consisting of four first-level indicators and twelve second-level indicators. The study aims to provide a theoretical foundation for reforming the training model of Master of Agricultural Management programs in universities.

The development of new quality productive forces calls for high-caliber innovative talent, and the cultivation of interdisciplinary and innovative professionals depends on the optimization and upgrading of educational paradigms in terms of educational philosophy, cognition, and practice. A retrospective examination of the evolution of educational paradigms reveals a mutually reinforcing relationship between education and the development of productive forces. On the one hand, productive forces shape the disciplinary structure, organizational forms, and developmental objectives of education; on the other hand, educational paradigms promote the growth of new quality productive forces by providing intellectual support and cultivating talent reserves. After more than a century of transformation, the educational paradigm has reached a critical juncture marked by both unprecedented opportunities and significant challenges. How education can further consolidate its role as a source of innovation for social development has become an urgent issue. From the perspective of cultural reflection, it is particularly important to understand the underlying logic of constructing a smart education paradigm. This requires the development of a smart educational ecosystem characterized by a high degree of humanistic engagement in its resources, interactions, and evaluation mechanisms; the continuous pursuit of stronger integration between knowing and doing within a closed-loop teaching and learning process; and a closer alignment between knowledge management and the cultivation of comprehensive competencies. Employing the smart education paradigm as a compass to navigate the diverse possibilities of future educational life represents a key direction for educational exploration in the age of artificial intelligence.

Digital capitalism is not merely an upgraded form of traditional capitalism; rather, it represents a reconfiguration of social relations arising from the deep integration of capitalist logic and digital technology. Under this new regime, users' everyday communicative activities are appropriated by capital and transformed into an implicit, non-waged form of data-producing labor, trapping digital interaction in the paradox of an appearance of freedom and an essence of domination. Digital communication exhibits four interrelated forms of alienation, while data fetishism obscures the exploitative nature of this process. Rooted in historical materialism, Marx and Engels' theory of intercourse begins with material social interaction, reveals the dialectical relationship between the development of productive forces and the forms of human intercourse, and, through its critique of alienated social relations, points toward the emancipation of humanity. As such, it provides an essential theoretical framework for analyzing the dilemmas of digital communication. Overcoming this alienation requires both the socialization of the means of communication and the democratization of communicative relations, thereby transcending the logic of capital through institutional transformation and human agency and reconstructing a digital communicative ecology grounded in universal human interaction.

As inclusive education expands globally, general school teachers face systemic challenges in bridging the gap between classroom realities and mandated professional competencies. Using a qualitative grounded theory design, this study decodes the developmental trajectory of special education literacy among general school teachers. Data were generated through theoretical sampling, comprising in-depth interviews with eight teachers across diverse regional contexts and a focus group with seven educational stakeholders; the theoretical saturation of the emerging model was rigorously verified via constant comparative analysis. The findings conceptualize a three-stage developmental continuum—progressing from intuitive experience to professional practice, and ultimately to reflective professionalism—cross-cut by five dynamic dimensions: cognitive appraisal, instructional practice, institutional support, home–school collaboration, and professional learning. Crucially, the study uncovers a structural decoupling within this trajectory, conceptualized as the "Instructional Substitution Effect", wherein teachers' technical deficits compel a reliance on defensive behavior management over substantive differentiated instruction. By addressing this "practice-ahead-of-cognition" asymmetry, this study advances inclusive pedagogical theory and provides a diagnostic blueprint for designing localized, scaffolded teacher professional development and structural school support eco-systems.

How to Explain the "Principles" in Ideological and Political Theory Courses provides a profound exposition of the essence of Ideological and Political Theory (IPT) courses and explores their practical implementation. As a matter of fundamental national importance, the essence of IPT courses lies in reasoning—that is, using young people's concerns, the needs of the nation, and the transformations of the times as entry points to integrate academic theory, moral reasoning, and philosophical insight. Centered on the question of how to effectively explain the principles embodied in IPT courses, the book develops a methodological framework that progresses from presenting principles accurately to presenting them vividly, with the aim of unifying theoretical rigor and pedagogical appeal. This requires IPT courses to embody both a firm political orientation and intellectual attractiveness, making abstract principles understandable and emotionally compelling through approaches such as narrative-based teaching. The construction of a discourse system for IPT courses bears directly on young people's values and beliefs, the fulfillment of the fundamental mission of fostering virtue through education, and the cultivation of a new generation capable of shouldering the historic task of national rejuvenation.

Generative Artificial Intelligence (GenAI) has raised concerns about academic integrity, cognitive dependence, professional roles, educational quality, and equitable participation. Research tends to report these concerns as a unified list of risks, even though their educational significance varies with the task in which AI is envisaged or applied. This study analysed a separately curated, risk-inflated corpus of 3,000 public-text records from six Chinese social media platforms and and 300 source Uniform Resource Locators (URLs). Five non-exclusive risk constellations and five educational task contexts were reconstructed from indexed keyword fields. Binary logistic generalised estimating equations clustered by source URL modelled task associations while adjusting for platform and text length. Cognitive-reliance language formed 35.5% of the corpus, academic-integrity language 22.7%, and teacher-role disruption 18.4%. Assignment/writing and assessment/feedback contexts were strongly associated with academic-integrity language (OR = 3.59, 95% CI [2.86, 4.51]; OR = 3.56 [2.90, 4.37]). Teacher-work contexts were associated with teacher-role disruption (OR = 1.39 [1.10, 1.74]). Variation between platforms was slight for four configurations; teaching-quality concern exhibited modest distinction. The results advocate task-based governance that combines process evidence with assessment, verification with teacher judgment, and access support with institutional policy. Since the corpus was intentionally risk-inflated, its percentages characterise semantic composition rather than platform-user prevalence.

To compare the effectiveness of an AI-empowered blended teaching model with that of the traditional teaching model in geriatric nursing education through an anonymous questionnaire survey. Results: The overall teaching effectiveness of the AI-empowered blended teaching model (3.79 ± 0.90) was significantly higher than that of the traditional teaching model (3.02 ± 1.08) (p < 0.05). In terms of comprehensive competence in community-based practice, the AI-blended teaching group also reported a significantly higher total self-assessment score (3.80 ± 1.17) than the traditional teaching group (2.81 ± 1.23) (p < 0.05). Students expressed positive attitudes toward the role of AI tools in supporting self-directed learning, particularly recognizing their value in objectively evaluating the strengths and weaknesses of their own care plans. Conclusion: The AI-empowered blended teaching model demonstrates superior educational outcomes compared with the traditional teaching model in geriatric nursing courses and is worthy of broader adoption in geriatric nursing education.

In the digital era, on the basis of reconstructing educational content and transforming educational modes, educational fields, as well as the subject-object relations in education, generative artificial intelligence embeds and optimizes the entire process of teaching and learning. At the teacher level, generative artificial intelligence forms an intelligent, interactive and human-machine collaborative teaching form. At the student level, it helps build an immersive, participatory and autonomous learning model. At the educational nurturing level, it strengthens value guidance through data-driven intelligent algorithms. Nevertheless, the transformation of education driven by generative artificial intelligence still faces multiple challenges. Human-computer interaction squeezes the space for real communication between teachers and students; algorithmic bias undermines the effectiveness of education; information narrowing erodes educational fields; and the expansion of instrumental rationality obscures students' subjectivity. Guided by practical problems, we shall improve the institutional mechanisms for empowering high-quality educational development with digital intelligent technologies, integrate educational resources and optimize educational content via generative artificial intelligence, build sound educational fields for teaching practice, and construct an integrated educational community of "teachers—machines—students".

Against the backdrop of the continued advancement of educational digitalization, generative Artificial Intelligence (AI) has rapidly become integrated into university students' course learning, information retrieval, assignment completion, and everyday information processing. Based on 342 undergraduate survey responses from X University, supplemented by unstructured interviews, this study employs literature review, questionnaire survey, and descriptive statistical analysis to investigate the current use of generative AI among university students, the manifestations of AI dependence, its consequences, and corresponding guidance strategies. The findings reveal that generative AI has become a high-frequency learning tool for undergraduates at X University. Students primarily use AI for after-class learning, information retrieval, routine question answering, and assignment assistance. Their dependence on generative AI is mainly manifested in three dimensions: functional dependence, cognitive dependence, and judgmental dependence. Although generative AI contributes positively to improving learning efficiency, broadening access to information, and facilitating the organization and expression of ideas, it may also weaken students' capacity for independent thinking, encourage shortcut-oriented learning behaviors, and diminish their awareness of academic norms. To achieve a balance between technological empowerment and students' autonomous development, universities should establish a coordinated guidance framework encompassing the cultivation of student agency, improved assignment design and process-based assessment by instructors, institutional regulations, and AI literacy support.