The incorporation of artificial intelligence (AI) into language education has created new opportunities for improving the instruction and acquisition of Chinese characters. Nevertheless, the cognitive difficulties linked to the acquisition of Chinese characters, such as their intricate visual features and lack of clear meaning, necessitate thoughtful deliberation when developing AI-supported learning interventions. The objective of this project is to explore the capacity of a collaborative method between humans and machines in teaching Chinese characters, utilising the advantages of both human expertise and AI technology. We specifically investigate the utilisation of ChatGPT, a substantial language model, for the creation of instructional materials and evaluation methods aimed at teaching Chinese characters to individuals who are not native speakers. The study utilises a mixed-methods approach, which involves both qualitative examination of lesson plans created by ChatGPT and quantitative evaluation of student learning outcomes. The results indicate that the suggested framework for human-machine collaboration can successfully tackle the cognitive difficulties associated with learning Chinese characters, resulting in enhanced learner involvement and performance. Nevertheless, the research also emphasises the constraints of AI-generated material and the significance of human involvement in guaranteeing the accuracy and dependability of educational interventions. This research adds to the expanding collection of literature on AI-assisted language learning and offers practical insights for educators and instructional designers who aim to use AI tools into Chinese language curriculum. The results emphasise the necessity of employing a multi-disciplinary strategy in AI-supported language learning, incorporating knowledge from cognitive psychology, educational technology, and second language acquisition.

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.

E-learning has become an integral part of higher education, significantly influencing the teaching and learning landscape. This study investigates the impact of student characteristics such as gender, grade, and major on E-learning satisfaction. Utilizing Structural Equation Modeling (SEM) and collecting data through 527 valid questionnaires from Nanjing Normal University students, this research reveals the nuanced relationships between these variables and E-learning satisfaction. The findings indicate that gender, grade, and major significantly and positively impact student satisfaction with E-learning, highlighting the need for tailored E-learning resources to meet diverse student needs. The study underscores the importance of continuous improvement in E-learning resources and platforms to enhance student satisfaction. This research contributes to the understanding of effective E-learning strategies in higher education institutions.

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 study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.

The present study, developed under a quantitative approach, explanatory scope and causal correlational design, aims to determine the influence of invisible learning on the research competence of high school students in two private schools in the city of Lima, Peru, whose educational models seek to develop autonomous learning and research through discovery learning and experimentation. Two questionnaires were applied to 120 students of the VII cycle of basic education, one to measure the perception regarding invisible learning with 20 items and the other to measure investigative competencies with 21 items; both instruments underwent the corresponding validity and reliability tests before their application. Among the main findings, descriptive results were obtained at a medium level for both variables, the correlations found were significant and moderate, and as for influence, the coefficient of determination R² yielded a value of 0.13, suggesting that 13% of investigative competence is predicted by invisible learning. These results show that autonomy, the use of digital technologies, metacognition and other aspects that are part of invisible learning prepare students to solve problems of varying complexity, allowing them to face the challenges of contemporary knowledge in an innovative and effective manner.