This study investigates the intricate causal mechanisms of university students’ sustained use of artificial intelligence (AI) tools for self-directed learning (SDL) within the theoretical framework of self-determination theory (SDT). Employing a convenience sampling strategy, 387 university students from China were included in the study. Methodologically, we employed structural equation modeling (SEM) for the measurement and causal analysis, and we employed fuzzy-set qualitative comparative analysis (fsQCA) for the configurational analysis. The research results emphasize several important insights. Perceived usefulness, intrinsic motivation, and satisfaction play important roles in encouraging university students’ continuance intentions. Satisfaction emerges as a pivotal mediator, bridging the connection between perceived usefulness, intrinsic motivation, and continuance intention. The system quality, the information quality, and social interaction have significant positive influences on perceived usefulness. Perceived autonomy and perceived competence display strong correlations with both intrinsic motivation and satisfaction. Moreover, the results from the fsQCA show five configurations, in which the key factors collectively shape students’ continuance intentions through complex interactions through various configurations. The findings reveal diverse configurations by which university students form continuance intentions towards using AI tools for SDL, providing detailed insights into the profound and indirect impacts on forming continuance intention behaviors. This enriches and advances the current theoretical understanding.

The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion topic mining and SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms in China. Through a detailed analysis of the data, this study examines the distribution of positive and negative emotions and identifies six topics. The study further utilizes visual tools such as word clouds and heatmaps to present the research findings. The results indicate that the emotional polarity across all topics is characterized by a predominance of positive emotions over negative ones. Moreover, an analysis of the keywords across the six topics reveals that each has its own emphasis, yet there are overlaps between them. Therefore, this study, through quantitative methods, also reflects the complex interconnections among the elements within the educational ecosystem. Additionally, this study integrates the six identified topics with the Technology–Organization–Environment (TOE) framework to explore the broad impact of AIGC on education from the perspectives of technology, organization, and environment. This research provides a novel perspective on the emotional attitudes and key concerns of the Chinese public regarding the use of AIGC in education.

Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. This study explores how game-based learning, supported by situated learning theory and game design elements, can boost learner motivation and knowledge construction. Using 20,293 user comments from the Chinese video platform Bilibili, the study applies sentiment analysis and LDA to uncover users’ sentimental tendencies and cognitive themes. The analysis identifies four core themes: (1) The application of contextual strategies in language learning, (2) Autonomous exploration and active participation in gamified learning, (3) Progressive enhancement of logical thinking in gamified environments, and (4) Teaching innovation in promoting knowledge construction and deepening. Building on these findings, the study further develops a cognitive scaffolding model integrating “contextualization–procedural logic–active construction” to explain the mechanisms of motivation–cognition interaction in gamified learning. Methodologically, this study innovatively combines LDA topic modeling with sentiment analysis, offering a new approach for multidimensional measurement of learner attitudes in gamified education. Theoretically, it extends the application of situated learning theory to digital education, providing systematic support for instructional design and meaning-making. Findings enrich empirical research on gamified learning and offer practical insights for optimizing educational platforms and personalized learning support.

With the rapid integration of artificial intelligence (AI) technologies in the field of education, public sentiment towards this development has gradually emerged as an important area of research. This study focuses on the sentiment analysis of online public opinions regarding the application of AI in education. Python was used to scrape relevant online comments from various provinces in China. Using the SnowNLP algorithm, sentiments were classified into three categories: positive, neutral, and negative. The study primarily analyzes the spatial distribution characteristics of positive and negative sentiments, with a visualization of the results through Geographic Information Systems (GIS). Additionally, Moran’s I and Getis-Ord Gi* are introduced to detect the spatial autocorrelation of sentiment attitudes. Furthermore, by constructing a multivariable geographical detector model and MGWR, the study explores the impact of factors such as the development of the digital economy, the construction of smart cities, local government policy attention, the digital literacy of local residents, and the level of education infrastructure on the distribution of sentiment attitudes. This research will reveal the regional disparities in AI and education-related online public sentiment and its driving mechanisms, providing data support and empirical references for optimizing the application of AI in education.