Writing has been recognized as a core competency for postgraduate students. However, improving writing performance, particularly for first-year postgraduate students, remains an important and challenging task. This study aimed to explore the effects of student engagement in peer feedback on writing performance from cognitive, affective and behavioral perspectives. Specifically, the participants were 88 postgraduate students. With the support of an online writing and peer review system (Peerceptiv), participants were asked to write an essay, engage in the peer feedback process, and revise their essays. Correlation analysis and multiple regression analysis were utilized to analyze data on the quality of participants’ essays, peer feedback procedures, and interviews. Results showed that cognitive engagement, affective engagement, and behavioral engagement were significantly associated with postgraduate students’ writing performance. The analysis indicated that the helpfulness of provided comments was the strongest predictor of writing performance, followed by suggestion, integration, and positive affective engagement. These findings highlight the significance of student engagement in peer feedback to improve writing performance in higher education. Recommendations for future research on quality improvement in writing performance are suggested based on the findings.

The widespread application of Generative Artificial Intelligence (Gen-AI) is transforming educational practices and driving pedagogical innovation. While cultivating higher-order thinking (HOT) represents a central educational goal, its achievement remains an ongoing challenge. Current evidence regarding the impact of Gen-AI on HOT is relatively fragmented, lacking systematic integration, particularly in the analysis of moderating variables. To address this gap, a meta-analysis approach was employed, integrating data from 29 experimental and quasi-experimental studies to quantitatively assess the overall impact of Gen-AI on learners’ HOT and to examine potential moderating factors. The analysis revealed that Gen-AI exerts a moderate positive effect on HOT, with the most significant improvement observed in problem-solving abilities, followed by critical thinking, while its effect on creativity is relatively limited. Moderation analyses further indicated that the impact of Gen-AI is significantly influenced by experimental duration and learners’ self-regulated learning (SRL) abilities: effects were strongest when interventions lasted 8–16 weeks, and learners with higher SRL capacities benefited more substantially. Based on the research findings, this study proposed that Gen-AI should be systematically integrated as a targeted instructional tool to foster HOT. Medium- to long-term interventions (8–16 weeks) are recommended to enhance learners’ problem-solving and critical thinking abilities. At the same time, effective approaches should also be explored to promote creative thinking through Gen-AI within existing pedagogical frameworks. Furthermore, individual learner differences should be accounted for by adopting dynamic and personalized scaffolding strategies to foster SRL, thereby maximizing the educational potential of Gen-AI in cultivating innovative talents.

Given the importance of student engagement for educational success, it is essential to explore how teachers can stimulate student engagement in online learning environment. However, relatively little research drawing from self-determination theory has examined the links between teaching motivations and student engagement. To this end, this study was conducted to survey 414 Chinese college students’ perceptions of teaching motivations, their own intrinsic and extrinsic motivations during the learning process, as well as their engagement with online learning. The findings indicated that the survey had satisfactory validity and internal consistency. Structural equation model revealed the interrelationships between autonomy-supportive teaching motivations, controlling teaching motivations, student intrinsic and extrinsic motivations, and student engagement. The results showed that in online learning environments, autonomy-supportive teaching motivations and student intrinsic motivation were positively related to student engagement. Unexpectedly, controlling teaching motivations and student extrinsic motivation had no significant effect on student engagement. Moreover, the mediating effects of student intrinsic motivation including perceived autonomy, competence, and relatedness offered a deeper understanding of the association between autonomy-supportive teaching motivations and student engagement. The main findings and practical implications together are discussed in depth. Implications for practice or policy: Teachers could adopt autonomy-supportive teaching strategies and stimulate students’ intrinsic motivation as they have shown to be positive factors for student engagement. Developers and educators could enhance student engagement through nurturing inner motivational resources in online learning environment. Researchers could verify more factors that influence student engagement and clarify how they could be manipulated in future studies.

With the development of China, learning the Chinese language has received more and more attention from international students. Teaching Chinese as a foreign language (TCFL) is crucial for spreading Chinese culture and promoting cultural exchanges. In recent years it has become the focus of attention of many scholars, and the heterogeneous cultural backgrounds and diverse language needs of learners determine that TCFL should promote the development of students’ intercultural communication competence in language practice with the use of educational technology. In this study, 82 preservice teachers as an experiment group and 64 preservice teachers as a control group participated in this study; this research aims to improve the quality and level of TCFL, using innovative teaching tools as a means to target preservice TCFL teachers and create a teaching–learning design strategy based on the smart teaching tools Moso Teach (MT) and Rain Classroom (RC). The findings from this research show the importance of the use of smart teaching tools and the improvement of preservice teachers during the experiment compared to preservice teachers with traditional methods. This paper answers the following questions: (1) How to improve preservice TCFL teachers’ competencies by integrating smart teaching tools into their learning process? (2) How to integrate technological, pedagogical, didactic, and social competencies into preservice teachers’ daily teaching CFL?

Background and objectives: Concern exists regarding the potential negative consequences of smartphone addiction among adolescents. This study investigated the effect of use motivations and alexithymia on smartphone addiction among adolescents with two insecure attachment styles, namely, anxious and avoidant attachment. These attachment styles were regarded as mediating variables.Methods: Self-report measures were used to assess use motivations, alexithymia, insecure attachment and smartphone addiction. Data were collected from 748 junior high school students (382 males and 366 females) in northeastern China. Structural equation modeling was used to test our hypothesis that use motivations and alexithymia are directly associated with smartphone addiction and also through the mediation of attachment insecurities.Results: The structural equation modeling results showed a strong and positive correlation between use motivation and smartphone addiction, with avoidant attachment mediating such a relationship. Meanwhile, the two components of alexithymia, difficulty identifying feelings and externally oriented thinking, positively predicted smartphone addiction, with avoidant attachment serving as a mediator of this effect. In addition, the mediation analysis results demonstrated that attachment anxiety mediated the connection between escape drive, extrinsically focused thought, and smartphone addiction.Conclusion: Findings describe how attachment insecurities, smartphone use motivations, and alexithymia can interact with one another to predict smartphone addiction. Smartphone use motivation types and alexithymia symptoms should be taken into consideration when designing targeted intervention programs for smartphone addiction to address the different attachment needs of adolescents, which would be helpful to reduce their smartphone addiction behaviors.