Currently,artificial-intelligence(AI)education serves as a vital component for cultivating internationally competitive talents.Relying on systematic policy design and practical deployment,Singapore has been actively charting effective pathways for AI education.This paper sorted out ten AI education initiatives embedded in relevant policies including Singapore's Digital Education Blueprint,Smart Nation programme,National AI Strategy and Student AI Outreach Scheme.Combined with interviews with thirteen Singaporean teachers engaged in AI education,five core experiences of Singapore's AI education were summarized:sustainable training pathways for AI teachers,interconnected AI education ecosystems,collaborative school-based AI education mechanisms,diversified industry-education integration systems for AI,and responsible governance regulations for AI deployment.Finally,this paper proposed suggestions to optimize the top-level design of AI education in China.By deepening teacher training mechanisms,integrating industry,academia,research and teaching,establishing a tiered curriculum certification system,and improving the governance of AI education,valuable references were provided for the development of AI education in China,so as to cultivate talents of the times equipped with AI literacy,technical competence and social responsibility.

Cultivating students' computational thinking (CT) has become an educational priority, with the implementation of effective instructional programs identified as a key means of fostering these skills. However, traditional mathematics teaching in many contexts often relies on lecture-based approaches that emphasize procedural skills over problem-solving, which may limit students' engagement and CT development. This study designed a CT-integrated mathematics instruction framework for secondary mathematics, supported by GGB programming, and conducted a case study to evaluate its efficacy. Using an experimental research approach, 74 middle school students from a public school in Hangzhou, China were divided into an experimental group and a control group. The experimental group received CT-integrated mathematics instruction, while the control group followed conventional teaching methods. Data were collected using the Computational Thinking Scale (CTS) and a Mathematics Learning Interest Inventory for quantitative analysis, supplemented by qualitative insights from student interviews. The results indicate that the experimental group demonstrated significantly higher levels of CT and greater interest in mathematics compared to the control group, with the most notable improvement observed in algorithmic thinking.

The application of social cognitive theory has expanded to the boundaries of human-computer interaction research. However, existing research has scarcely addressed mutual cognitive facilitation between humans and personalized educational large language model (LLM) agents. This study explored how educational LLM agents influence teachers' curriculum design and content creation, based on a sample of 464 teachers from coastal regions of China, along with semi-structured interviews with 23 participants. Quantitative analysis of the survey data revealed that the involvement of educational LLM agents positively predicts teachers' ability to create content in curriculum design. Additionally, teachers' self-efficacy mediated this relationship, while both school support and self-efficacy together created a chain mediation effect. Qualitative findings from the interviews supported the quantitative results and further highlighted individual differences and contextual nuances in teachers' use of educational LLM agents. In summary, the findings indicated that educational LLM agents positively impact teachers' curriculum design and content creation, with school support and teachers' self-efficacy acting as a chain mediator in this process.

This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine university students' adoption of Large Language Model (LLM) Agents for self-directed learning. Using a mixed-methods approach (306 questionnaires, 9 interviews) incorporating Information Accuracy and Personal Innovativeness, results show that Performance Expectancy and Social Influence jointly exert the strongest direct impact on Behavioral Intention. Crucially, Information Accuracy acts as a cognitive contract influencing usage directly and indirectly. Personal Innovativeness indirectly drives intention by significantly enhancing Performance Expectancy. Conversely, Effort Expectancy is neutralized, showing no significant impact, while Facilitating Conditions and intention jointly explain over half the variance in actual Usage Behavior. These findings rigorously refine the UTAUT framework for the agentic AI era, underscoring that functional utility, factual trustworthiness, and robust institutional support, rather than mere usability, are the true catalysts for integrating LLM Agents into autonomous learning ecosystems.

In the rapidly evolving educational landscape, understanding how school support influences the content creation of pre-service teachers’ instructional design is crucial for fostering effective teaching practices and sustainable professional development. This study aims to explore the influence pathways and mechanisms through which school support affects the content creation of pre-service teachers’ instructional design. A total of 871 Chinese pre-service teachers were surveyed using an online questionnaire to assess school support, generative AI technology, self-efficacy, and instructional design content creation. The results indicate that school support has a significant positive predictive effect on the content creation of pre-service teachers’ instructional design. Moreover, generative AI technology and self-efficacy of pre-service teachers play a chain mediating role between school support and instructional design content creation. To enhance the content creation of pre-service teachers’ instructional design and promote the sustainability of teachers’ professional development, it is recommended that emphasis be placed on the application of school support and generative AI technology, as well as the enhancement of self-efficacy of pre-service teachers.

BACKGROUND:Artificial intelligence (AI), as a smart and connected technology, has significantly expanded the educational landscape.As more educators and learners begin to rely on GAI to assist with tasks such as instructional design and information generation, its potential to support problem-solving has gained increasing attention. However, the extent to which GAI contributes to the development of higher-order thinking skills-such as creative thinking, critical thinking, and metacognitive awareness-and how these thinking processes interact to influence problem-solving ability remains underexplored. A more comprehensive understanding of this relationship is needed to guide the effective integration of GAI in educational practice. METHODS:The study encompassed 473 pre-service teachers from three distinct higher education institutions, with specialties in science, computer science, and mathematics, and included a four-week generative AI-supported instructional design training program. And a semistructured interview comprising open-ended questions was administered to 50 pre-service teachers within the experimental group to present their views on generative AI-assisted teaching. Assessments were conducted before and after this program using a higher-order thinking skills survey. The relationship between thinking and problem-solving ability development of pre-service teachers using generative artificial intelligence was analyzed with a moderated model. RESULTS:The study encompassed 473 pre-service teachers from three distinct higher education institutions, with specialties in science, computer science, and mathematics, and included a four-week generative AI-supported instructional design training program. And a semistructured interview comprising open-ended questions was administered to 50 pre-service teachers within the experimental group to present their views on generative AI-assisted teaching. Assessments were conducted before and after this program using a higher-order thinking skills survey. The relationship between thinking and problem-solving ability development of pre-service teachers using generative artificial intelligence was analyzed with a moderated model. CONCLUSIONS:This study confirms that training pre-service teachers using generative AI to foster creative thinking can elevate their critical thinking, thereby impacting their problem-solving abilities. Moreover, metacognitive thinking amplifies the impact of creative thinking on critical thinking, resulting in a moderated mediation effect.

This article explores whether there are significant differences in the creativity level of future teachers in teaching design. In virtual reality (VR) and mixed reality (MR) experimental teaching environments, the K-DOCS creative power scale, the flow state scale, and the expert group assessment scale were used to investigate the mediation effect of flow, attention, and meditation on the creativity level. The results indicate that in the MR experimental teaching environment, the three factors have a moderate mediating effect on the creativity level of future teachers (n = 65), and meditation has a certain regulating effect on heart flow (0.175*); In the VR experimental teaching environment (n = 67), only the factor of heart flow has a moderate promotion effect on the creativity level of future teachers (0.822***). The study results indicate that the quality of future teachers' creative instructional design in the MR environment is higher than that in the VR environment. In addition more MR experimental teaching environments should be provided to cultivate the creativity level of future teachers in teaching design.

Generative artificial intelligence (AI) has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted preservice teaching skills training on preservice teachers' self-efficacy and higher order thinking. The participants of this study were 215 preservice mathematics, science, and computer teachers from a university in China. First, a pretest-post-test quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Finally, a semistructured interview comprising open-ended questions was administered to 25 preservice teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of preservice teachers in the experimental group, who used generative AI for teachers' professional development, were considerably higher than those of the control group, both in teacher self-efficacy (F = 8.589, p = 0.0084 < 0.05) and higher order thinking (F = 7.217, p = 0.008 < 0.05). It revealed that generative AI can be effective in supporting teachers' professional development. This study produced a practical teachers' professional development method for preservice teachers with generative AI.

This study investigates the impact of artificial general intelligence (AGI)-assisted project-based learning (PBL) on students' higher order thinking and self-efficacy. Based on input from 17 experts, four key roles of AGI in supporting PBL were identified: information retrieval, information processing, information generation, and feedback evaluation. An educational experiment was then conducted with 198 eighth-grade students from two middle schools in China, using a pretest and posttest design. The students were divided into three groups: Experimental Group A (AGI-assisted PBL), Control Group B (PBL without AGI assistance), and Control Group C (traditional teaching methods). A scale was administered to assess students' higher order thinking and self-efficacy before and after the experiment. In addition, semistructured interviews were conducted with 12 students from Experimental Group A to gather qualitative data on their perceptions of AGI-assisted PBL. The results indicated that students in Experimental Group A had significantly higher scores in higher order thinking and self-efficacy compared to those in Control Groups B and C, demonstrating the positive impact of AGI in supporting PBL learning.

PurposeThis paper seeks to investigate the differences in the teachers' professional development (TPD) by mentorship in workplace. The authors examined the role of mentorship in the PD of teachers and conducted a meta-analysis of pertinent empirical data.Design/methodology/approachUsing data from over 2,900 individuals, 66 experiments and 12 countries, the authors presented a meta-analysis of the association between workplace mentorship and TPD.FindingsThe authors concluded that mentoring activities could boost the TPD to some extent. It contributes positively to the discipline of science and language, kindergarten, individual mentoring and curriculum research. In addition, the periodicity should not exceed 1 year.Research limitations/implicationsThe results of the meta-analysis are restricted to short-term mentorship activities, and the sample size is modest. Building upon the findings from the literature review and meta-analysis, the authors delineated a research agenda for prospective investigations. This includes an imperative for further exploration into the nexus between mentoring and the PD of educators.Practical implicationsBased on the available literature and meta-analysis findings, the authors developed a framework for the "Experts in the classroom" TPD pattern.Originality/valueThis is the first meta-analysis evaluating the association between mentorship and TPD.

南非是非洲最具影响力的"金砖国家",南非《自然科学》和我国《科学》都是初中综合性课程教科书,在编写理念与选材方面有许多可相互借鉴之处.为了给我国未来支援非洲科学教育的志愿者提供参考教材以及为我国科学教科书编写和课程改革提供借鉴与启示,分别选取中国和南非初中科学教科书各6本为研究对象,在STEM跨学科视域下,采用定性结合定量的方法,就内容选取、编排结构、情境创设和目标导向等内涵开展质性分析,对教科书中物理、化学、生物、地理四大主题中STEM元学科下知识点分布及频次进行编码统计分析.研究发现,相较于我国初中科学教科书,南非《自然科学》教科书STEM理念注重工程教育和数学教育,内容选取侧重南非国情与通用知识相结合,内容编排重视以科学学科为中心与其他各学科的交互融合,情境创设强调情境的真实性与问题解决的有效性,目标导向注重STEM视域下的学生科学素养提升.对我国科学教科书编写和课程改革有如下启示:重视工程学教育,关注跨学科融合;关注跨学科概念整合,聚焦科学本质传递;依托理性思维活动,培养学生科学素养;教材内容编排方式,借鉴南非分科范式.

This paper proposes a blended teaching design model on WeChat Platform-based SPOC and carries out an empirical study of its applicability and effectiveness in lower-secondary school science teaching. Participants were from two eighth grade classes with different academic performance, and after conducting the same blended teaching, it was found that pre-class knowledge transmission on the WeChat platform considerably enhanced students’ classroom participation, which was also highly related to their improved post-class test scores. The improvement in the performance of the weak groups is more obvious. In addition, backend data from the WeChat platform revealed that the number of resource clicks was significantly higher than the total number of students, indicating that the sharing function of the WeChat platform facilitated the sharing of educational resources. Seven in-service science teachers were also interviewed to qualitatively evaluate and analyze the teaching model from the perspective of in-service teachers. The comprehensive results indicate that the teaching model constructed in this study achieves good results, facilitates the development of students’ independent learning ability, and allows for the integration of this blended teaching model into lower-secondary curriculum. Keywords: Blended teaching; Science education; SPOC; Teaching design model; WeChat platform-assisted teaching

Social media usage is indispensable for college students, but the connection between social media and learning has received little scientific investigation. By examining pre-service teachers' attention to science, technology, engineering, and mathematics (STEM) teaching content and presentation in social media apps through WeChat, DingTalk, and TikTok, this study aimed to provide suggestions on using social media apps to promote pre-service teachers' skill learning and teaching development and to understand the relationship between social media and learning. 383 valid surveys were distributed and gathered. The findings indicate that: 1) Social media apps have both beneficial and detrimental effects on education. 2) The degree of agreement differs on "Social media app is an excellent teaching tool" and "social media app has significant promise in boosting educational development". The highest and lowest levels of agreement degrees were obtained for DingTalk and TikTok. The level of identification also affects how much pre-service teachers may pay attention to educational research and how frequently they study new materials in the future. 3) The degree to which pre-service teachers' academic performance in professional learning is affected by their use of social media varies. These findings have implications for pre-service teachers. This study suggests that it is necessary to further investigate the teaching aid function of social media apps and how pre-service teachers can better utilize them to develop professional skills.

The construction of business incubator platforms to assist young people who return to their hometowns to launch their own enterprises is urgently needed because youth entrepreneurship is seen as a crucial component of rural revitalization. Based on this, the authors of this study distributed surveys to 468 returning youths in rural startup spaces to gather data, built a structural model, and conducted interviews with 13 entrepreneurial youths to examine the relationship between government policies, services, and the design of rural startup spaces, as well as the self-efficacy of returning entrepreneurial youths and the innovation performance of businesses. The results demonstrate how important government policies are in encouraging youth entrepreneurship in their local communities. It has the potential to enhance both the development of rural crowdsourcing spaces and the self-efficacy of young entrepreneurs, thereby enhancing the innovative capabilities of local entrepreneurial businesses. Therefore, in order to encourage economic development in rural areas, the government should improve pertinent support measures, enhance the development of business incubation platforms, and encourage young people moving back to their hometowns to start their own businesses.