Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

The purpose of this study is to explore the relationship among higher vocational college (HVC) students’ social support (SS), learning burnout (LB), and learning motivation (LM), and to further explore the influence regulation mechanism. By analyzing the questionnaire survey data of 500 HVC students, this study found some important conclusions. First, a positive correlation is found between SS and LM, whereas LB exhibits a negative correlation with LM. Second, regression analysis results indicate significant influences of SS and LB on LM, with the latter serving as a partial intermediary between SS and LM. Lastly, analysis of group disparities reveals noteworthy distinctions in SS, LB, and LM across students of varying grades. These discoveries underscore the pivotal roles of SS and LB in molding the LM of HVC students, offering valuable insights for educational practices and policy recommendations. This study benefits the understanding of the key factors in the learning process of HVC students and provides a new direction for further research.

This research delves into the intricate dynamics of ethical leadership within the context of Vietnamese Small and Medium Enterprises (SMEs). By scrutinizing its impact on organizational effectiveness, the study unveils a comprehensive understanding of the interconnectedness between ethical leadership, knowledge sharing, and organizational learning. Employing a mixed-methods approach, the research investigates the mediating roles played by knowledge sharing and organizational learning in the relationship between ethical leadership and organizational effectiveness. Through empirical analysis and case studies, this study contributes valuable insights to the literature, offering practical implications for fostering ethical leadership practices in Vietnamese SMEs to enhance overall organizational effectiveness. The findings shed light on the nuanced mechanisms through which ethical leadership contributes to sustainable success, emphasizing the pivotal roles of knowledge sharing and organizational learning in this intricate relationship.

This quasi-experimental study examined the effect of a mechanics course delivered through a Learning Management System (LMS) on the creativity of prospective physics teachers at a teacher training college in Mataram, Indonesia. The study was conducted in the post-pandemic era. Using a pretest-posttest one-group design, the researchers evaluated changes in creativity across three domains: figural, numeric, and verbal. The results showed significant improvements in overall creativity, with the most critical gains observed in the figural domain. Further analysis revealed that fluency was the creative indicator with the most enhancement. In contrast, other indicators displayed varying degrees of improvement. These findings highlight the potential of LMS-based instruction in fostering creativity among future physics educators, particularly in the figural, numeric, and verbal domains. This study adds to the growing body of evidence supporting technology integration into teacher education, especially during times of crisis. Future research should explore more targeted instructional strategies within LMS environments and utilize comprehensive creativity assessment methods further to enhance creative learning experiences for prospective physics teachers.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.