Adaptive learning is an approach toward personalized learning and places the concept of “learner-centered education” into practice. With the rapid development of artificial intelligence and other technologies in recent years, there have been many breakthroughs in adaptive learning. Thus, it is important to gain insight into the evolution of related research and to track the research frontiers to further promote its development. This study used CiteSpace and VOSviewer to conduct a bibliometric analysis of 644 adaptive learning journal papers indexed in the WoS database from 2000 to 2022. This study presented a general view of the field of adaptive learning research over the last two decades using quantitative analysis. Currently, adaptive learning research is rapidly developing. In terms of the major research forces, a core group of authors including Qiao J. F., Han H. G. and Song Q has been formed; the major publishing country in this field is China; the core publishing journals include IEEE Transactions on Neural Networks and Learning Systems. Four major research topics in this field were identified using cluster analysis, namely the application of deep learning in educational data analysis, the development and application of adaptive learning model in AI education, the development and application of intelligent tutoring system in tutoring and teaching, cutting-edge modeling technology for feature modeling and knowledge tracing. Through evolution analyses, the logic of adaptive learning research’s development was determined; that is, technological changes have played a key role in the development of this field. Following the logic, we presented three frontiers of adaptive learning with burst terms: feature extraction, adaptation model and computational modeling. Adaptive learning is a core research topic for both computer science and educational technology disciplines, and it is also an important field where emerging technologies empowering education and teaching can play a part. The findings of the study clearly presented the current research status, evolutionary logic and research frontiers of this topic, which can provide references for the further development of this research field.

Purpose: This study aims to examine how virtual influencers (VIs) affect purchase intentions in tourism and hospitality e-commerce live streaming (THCLS) by focusing on the roles of VIs’ source credibility, trust in products, trust in VIs, emotional engagement, parasocial relationships and influencer–product congruence. Design/methodology/approach: Survey data from 416 active viewers of VIs in THCLS were analysed using partial least squares structural equation modelling. Findings: This study highlights the importance of the VIs’ source credibility, which positively affects trust in the product, trust in VIs and emotional engagement. However, source credibility does not have a positive impact on parasocial relationships. Trust in products positively influences trust in VIs. Emotional engagement and trust in VIs significantly influence parasocial relationships, which, positively affects purchase intentions. Influencer–product congruence strengthens the link between parasocial relationships and purchase intentions but does not moderate the relationship between trust in VIs and purchase intentions. No significant gender differences were observed, although minor discrepancies were noted in the effect of trust in products on trust in VIs. The importance–performance map analysis revealed that parasocial relationships are the most important factor influencing purchase intentions, while influencer–product congruence has the highest performance, trust in products is the least important and VIs’ source credibility has the lowest performance. Practical implications: This study provides actionable insights for marketers leveraging VIs in the THCLS sector, emphasizing strategies to enhance VI credibility, foster parasocial relationships, ensure influencer–product congruence and adopt gender-neutral marketing approaches to effectively influence purchase intentions. Originality/value: This study offers theoretical and practical insights into the role of VIs in THCLS, illuminating their impact on consumer behaviour and purchase intentions.

The emergence of ChatGPT has sparked new expectations for AI-empowered educational transformation. However, it remains unknown which factors affect its effectiveness in empowering learners to solve programming problems. Therefore, this study employed a quasi-experimental research design and used Python graphing in programming education as an example to investigate the factors influencing the effectiveness of learners in applying ChatGPT to problem-solving. Findings: AI literacy significantly influences learners’ effectiveness in using ChatGPT to solve problems, with AI awareness and AI usage being key factors. The knowledge base of programming language significantly affects learners’ effectiveness in applying ChatGPT to solve programming problems. Learners’ cognitive level of ChatGPT significantly influences their effectiveness in applying ChatGPT to problem-solving, while usage intention does not have a significant impact. However, learners’ intention to use ChatGPT significantly improves after application. Based on these findings, this study proposes that in the process of empowering education with Artificial Intelligence Generated Content (AIGC) products, the focus on learners should shift from cultivating their AI usage to AI literacy, laying the foundation for empowering learning with AIGC products. It is suggested to shift from mastering specific knowledge to graph-based rules as a method for empowering learning with AIGC products. Additionally, the focus should shift from enhancing learners’ intention to use the technology to strengthen their technological awareness, thereby creating practical pathways for empowering learning with AIGC products.