Artificial Intelligence (AI) is reshaping higher education by enabling personalized learning (PL) and enhancing teaching and learning practices. To examine global research trends, pedagogical paradigms, equity and sustainability considerations, instructional strategies, learning outcomes, and interdisciplinary collaboration, this study systematically reviewed 29 articles indexed in the Social Sciences Citation Index (SSCI) Q1, representing the top 25% of cited articles, published between January 2020 and December 2024 in the Web of Science database. Results indicate that AI-PL research is concentrated in Asia, particularly China, and predominantly situated within education and computer science. Quantitative designs prevail, often complemented by qualitative insights, with supervised machine learning as the most common algorithm. While constructivist principles implicitly guide most studies, explicit theoretical grounding improves AI-pedagogy alignment and educational outcomes. AI demonstrates potential to enhance instructional approaches such as PBL, STEAM, gamification, and UDL, and to foster higher-order skills, yet uncritical use may undermine learner autonomy. Systematic attention to equity and SDG-related objectives remains limited. Emerging interdisciplinary collaborations show promise but are not yet fully institutionalized, constraining integrative system design. These findings underscore the need for stronger theoretical framing, alignment of AI with pedagogical and societal imperatives, and professional development to enhance educators’ AI literacy. Coordinated efforts among academia, industry, and policymakers are essential to develop scalable, context-sensitive AI solutions that advance inclusive, adaptive, and transformative higher education.

As an alternative to traditional verbal counselling, expressive arts therapy has been shown to be an effective method of mental health care, particularly when dealing with stressful public interactions, such as those associated with COVID-19. However, few studies have been conducted to determine the efficacy of expressive arts therapy on the resilience of psychologically exposed university students during COVID-19. Furthermore, since network analysis appears to be a popular approach in psychological research, it has not been used in recent intervention studies for resilience. As a result, the current study utilized a network analysis approach to determine the efficacy of expressive arts therapy on the resilience of university students during the COVID-19 pandemic. A total of 263 students in a comprehensive university in China were selected for the therapy group between March and November 2021. In a pre-post design, students’ resilience was assessed using the Resiliency Scale for University Students (RSUS). The extended Bayesian information criteria (EBIC) and graphical LASSO were used to estimate and define paired resilience networks, and the strength, betweenness, and closeness indices were utilized to determine the centrality of the six facets of resilience. Additionally, we verified the stability and accuracy. It was discovered that significant differences appeared between the paired networks before and after expressive arts therapy. Facets of self-efficacy, self-acceptance and problem-solving in resilience were notably improved after the therapy, with the variable of emotional stability sustained at the mean level. Meanwhile, the network analysis has highlighted the central variable of self-efficacy in the pre-intervention and support from friends in the post-intervention. The connectivity among the components of problem solving, support from friends, and support from family was enhanced, with support from friends playing the role of hub nod in the following network. By utilizing a network analytic approach, expressive arts therapy can be more targeted in intervening in resilience mechanisms. As a proxy for efficacious problem-solving, intervention should be calibrated to the cultivation of social support networks, especially in the support from friends.

Object: Repeated quarantine policies over the past 3 years have led to poor psychological consequences for the public. Previous studies have proved that the quarantine policy leaves individuals vulnerable to anxiety, depression, and insomnia, especially among college students. This study aims to explore whether psychological problems during isolation continue with the release of isolation.Methods: Overall, 2,787 college students both answered a web-based survey during and after the closure management was lifted. The Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Youth Self-rating Insomnia Scale were measured. The cross-lagged path model was used to explore the influence of psychological impact during isolation on the individual after the release.Results: We found that anxiety and sleep disturbance levels alleviated significantly after quarantine, except for depression. As expected, a bidirectional relationship exists between anxiety, depression, and sleep disturbance. Moreover, depression and sleep disturbance can predict post quarantine depression, sleep disturbance, and anxiety, yet anxiety cannot predict sleep disturbance afterward.Conclusion: Timely and effective intervention for anxiety, depression, and insomnia during isolation is essential for individuals to repair themselves quickly after the release.

This study integrates knowledge sharing into traditional teacher training to enhance teachers’ professional development. It addresses the research gap in implementing such integration by developing a video cloud platform that supports online training and knowledge-sharing among teachers. The proposed approach combines the SECI model (socialization, externalization, combination, and internalization) with the video cloud platform to create a new model of teacher e-learning, fostering collaborative learning among teachers in a remote and interactive context. To demonstrate the effectiveness of this approach, the study conducted experiments with Chinese primary and secondary school teachers. The new model was applied in an online professional competence-oriented supporting environment. The results showed a significant improvement in organization and management skills, and a moderate increase in technical literacy, learning, and development capabilities among the participating teachers. These findings have important implications for professional development of teachers, particularly in the context of the ongoing epidemic and the shift towards a networked and intelligent world. By integrating knowledge sharing into traditional training, teachers can enhance professional competence and adapt to the changing educational landscape. The video cloud platform is a valuable tool for facilitating collaborative learning and knowledge creation among teachers.