International Journal of Educational Science and Theory

This study aimed to examine the influence of recipient social behavior on risky decision-making among 4- to 6-year-old children in gain and loss contexts. A total of 210 Chinese children aged 4 to 6 years participated in a situational experimental task. The study investigated children’s risk preferences when making choices for recipients described as either prosocial or aggressive. The results showed that, in the gain context, children generally tended to make more risky choices for prosocial recipients and more conservative choices for aggressive recipients. In contrast, in the loss context, children generally tended to make more conservative choices for prosocial recipients and more risky choices for aggressive recipients. Age-related differences were also observed in the number of risky choices, although the pattern of age differences varied across decision contexts and recipient social behavior conditions. These findings suggest that when children make risky decisions for others, their choices may be influenced not only by gain/loss contexts, but also by the social behavior characteristics of the recipient.

As a vital domain of vocal art and linguistic expression, dubbing education faces persistent challenges in traditional instruction, including a shortage of qualified teachers, subjective evaluation, and insufficient personalization. From the perspective of AI+education, this study systematically reviews the latest national policies from 2025 to 2026 and constructs a three-tier technical framework consisting of the infrastructure layer, algorithm engine layer, and application scenario layer. Five core technical pathways are elaborated in detail: intelligent speech assessment, affective computing, personalized recommendation, virtual teacher synthesis, and data-driven governance. On this basis, a “human-machine collaborative” dual-teacher classroom model and an intelligent dubbing training platform are designed. The effectiveness of AI-assisted instruction is empirically verified through a quasi-experimental study. Results show that AI-enabled dubbing education reduces feedback latency from days to seconds, increases emotional expression matching by over 26%, and lowers teachers’ grading workload by 62%. This paper provides both theoretical reference and practical guidance for the digital transformation of vocal art education.

The transition from generative artificial intelligence to agentic AI represents a critical paradigm shift in educational technology. Focusing on personalized learning in adult higher education, this paper integrates sociocultural learning theory, human-computer interaction principles, and big data analytics to investigate the role reconstruction and pathway optimization of educational agents. We clarify four core capabilities of agentic AI, propose a typology of educational agent roles, and illustrate three application scenarios: Socratic questioner, collaborative information hub, and affective support monitor. We further analyze key challenges including the metacognitive participation paradox, data privacy, algorithmic bias, and black-box opacity. Grounded in a human-AI augmentation paradigm, we provide targeted mitigation strategies and multi-stakeholder recommendations. This study offers theoretical and practical insights for the integration of AI, big data analytics, and social sciences in educational transformation, supporting the realization of UN SDG4.

In the process of higher education globalization, transnational education intermediaries are commonly understood as functional actors that connect universities and students, occupying a relatively peripheral position within global educational mobility structures. While existing studies have predominantly examined their market behaviors at micro or case-based levels, this paper adopts a macro-theoretical perspective to explore how these intermediaries reshape their roles within specific institutional and power configurations. Integrating Bourdieu’s field theory with Foucault’s discourse–power framework, the study constructs an analytical model of “field–discourse–legitimacy” and introduces the concept of peripheral adaptation to explain how intermediaries, within the context of Commonwealth–China higher education cooperation, engage in differentiated discursive practices toward multiple power actors to facilitate capital conversion and legitimacy construction. The findings suggest that intermediary discourse should not be regarded merely as commercial expression; rather, it constitutes an institutionalized practice embedded within global knowledge–power structures. The relative movement from “periphery” to “center” reflects the growing participation and influence of non-state actors in the governance architecture of international higher education.

With the comprehensive victory of China's poverty alleviation campaign, rural poverty alleviation strategies have shifted from relief-based poverty alleviation to participatory poverty alleviation. The core lies in enhancing the comprehensive capabilities of rural low-income groups through education empowerment. However, rural low-income populations still face issues such as low income levels, weak income-increasing capacity, and low-level participation in industries. Based on education empowerment, this paper analyzes the dilemmas and causes of income growth for rural low-income groups from the perspective of the coordination between labor skills and working conditions, including insufficient resource endowments, low educational attainment, and lack of endogenous motivation. To address these problems, the paper proposes three income-increasing paths: the "skills-job" matching model, the "resource-industry" transformation model, and the "policy-market" linkage model. The study argues that only through the coordinated cooperation of the government, enterprises, and all sectors of society, and through education to continuously improve working conditions and enhance labor skills, can the sustainable income growth of rural low-income groups be achieved, and the goal of common prosperity be promoted.

Current dance education largely relies on physical presence and verbal instruction with bodily demonstration. The digital transformation of teaching research offices encounters distinctive theoretical dilemmas and practical tensions. Drawing on the theoretical frameworks of phenomenology of technology and tacit knowledge theory, this paper analyzes the functional spectrum, operation mechanism, and knowledge reconstruction paths of artificial intelligence intervention in the development of dance virtual teaching research offices. The study reveals that artificial intelligence is not merely an instrumental enabler. Instead, it transforms the mode of knowledge production for dance teaching research via three pathways: motion datatization, experiential publicization, and collaborative knowledge production. Meanwhile, underlying tensions emerge, including the conflict between the irreplaceability of physical presence and stylistic diversity in dance education, as well as the squeezing of humanistic attributes of teaching by quantitative evaluation. For the sustainable development of dance virtual teaching research offices, we should adhere to the principle of “human-technology symbiosis”, orient technological design toward bodily ontology, reconstruct the evaluation paradigm through a dual-track mechanism, and foster an educational field via institutional innovation.