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.

This paper examines the detrimental impact of rapid inflation on the quality of private education in developing countries. By focusing on the financial challenges faced by private schools, the study highlights the tension between education policy and economic realities. While private schools often attract parents with smaller class sizes and specialized programs, the core motivation lies in investing in children’s future through quality education. However, this study demonstrates how inflation can cripple this sector. The case of Turkey exemplifies this challenge. Post-pandemic inflation created a financial stranglehold on private schools, as rising costs made it difficult to adjust teacher salaries. This, in turn, led to teacher demotivation and a mass exodus, ultimately compromising educational quality. Furthermore, government interventions aimed at protecting parents from high tuition fees, through limitations on fee increases, inadvertently sacrificed the very quality they sought to safeguard. The paper concludes by advocating for alternative policy approaches that prioritize direct support for education system during economic downturns. Such measures are crucial for ensuring a strong and resilient education system that benefits all stakeholders, including parents, students, and the nation as a whole.

The promulgation of the Curriculum Standards for ordinary high School (2017 edition, 2020 revision) has effectively promoted the reform of high school mathematics classroom. In order to cope with the change of textbook content in the new curriculum reform, it has become one of the important tasks for high school mathematics teachers to implement teaching activities better and sort out and analyze the differences between the old and new textbooks. This paper analyzes the differences between old and new textbooks from the three dimensions of system structure, course content and example exercises, and gives some reasonable teaching suggestions. Among them, the new textbook uses 2019 "Ordinary High School Textbook" person-taught A version of Compulsory Mathematics 1, and the old textbook uses 2004 "Ordinary High School Mathematics Curriculum Standard Experimental Textbook" person-taught A version of compulsory Mathematics 4. In general, the adjustment of the new teaching materials is more in line with the cognitive characteristics of students, pay attention to the penetration of mathematical culture and pay attention to the development of students' mathematical core literacy.

In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.