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 Agriculture Trading Platform (ATP) represents a significant innovation in the realm of agricultural trade in Malaysia. This web-based platform is designed to address the prevalent inefficiencies and lack of transparency in the current agricultural trading environment. By centralizing real-time data on agricultural production, consumption, and pricing, ATP provides a comprehensive dashboard that facilitates data-driven decision-making for all stakeholders in the agricultural supply chain. The platform employs advanced deep learning algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to forecast market trends and consumption patterns. These predictive capabilities enable producers to optimize their market strategies, negotiate better prices, and access broader markets, thereby enhancing the overall efficiency and transparency of agricultural trading in Malaysia. The ATP’s user-friendly interface and robust analytical tools have the potential to revolutionize the agricultural sector by empowering farmers, reducing reliance on intermediaries, and fostering a more equitable trading environment.

This paper investigates the evolving clustering and historical progression of “Asian regionalisms” concerning their involvement in multilateral treaties deposited in the United Nations system. We employ criteria such as geographic proximity, historical connections, cultural affinities, and economic interdependencies to identify twenty-eight candidate countries from East Asia, Southeast Asia, South Asia, and Central Asia for this empirical testing. Using a social network analysis approach, we model the network of these twenty-eight Asian state actors alongside 600 major treaties from the United Nations system, identifying clusters among Asian states by assessing similarities in their treaty participation behavior. Specifically, we observe dynamic changes in these clusters across three key historical eras: Post-war reconstruction and transformation (1945–1968), Cold War tensions and global transformations (1969–1989), and post-Cold War era and globalization (1990–present). Employing the Louvain cluster detection algorithm, the results reveal the evolution in cluster numbers and changes in membership status throughout the world timeline. The results also identify the current situation of six distinct Asian clusters based on states’ inclinations to engage or abstain from multilateral treaties across six policy domains. These findings provide a foundation for further research on the trajectories of Asian regionalisms amidst evolving global dynamics and offer insights into potential alliances, cooperation, or conflicts within the region.