## 6 Limitations  
By integrating Multiscale Geographically Weighted Regression (MGWR) and Geo-Detector, this study successfully unveils the spatial distribution characteristics of public sentiment at the provincial level and its association with socio-economic factors, providing a solid quantitative foundation for understanding the acceptance of AI in education. However, several limitations remain, warranting further refinement in future research. Although the spatial analysis framework and driving mechanism exploration employed in this study exhibit strong theoretical generalizability and can serve as a methodological reference for other countries--especially in examining the relationship between technology adoption and sentiment--the cultural dimensions theory suggests that power distance and collectivist cultural traits are critical factors in the study of social behavior and governance. Therefore, it is important to acknowledge that this study is grounded in China's socio-political environment, where technological advancements shape public sentiment and its spatial distribution under strong governmental influence. Given China's highly centralized political system and rapid economic transformation, public perceptions are often significantly shaped by government-led policy initiatives (e.g., the enactment of relevant legislation) and mainstream official media narratives. In contrast, in culturally diverse or more individualistic societies (e.g., Western democracies), public acceptance of AI in education may be driven more by concerns over privacy ethics and technological fairness, rather than direct policy advocacy. This suggests that the evolution patterns and sentiment distribution may follow different trajectories across regions. Future studies should incorporate cross-cultural data from multiple countries to examine moderating effects of cultural, economic, and technological contexts, thereby testing the model's adaptability and boundary conditions and deepening the understanding of AI's global impact. Second, this study captures regional sentiment variations and key driving factors based on a limited temporal scope, focusing primarily on cross-sectional spatial analysis rather than longitudinal changes in public sentiment. The static nature of this design restricts insights into temporal trends and diffusion patterns of emotions. For instance, public acceptance of AI in education may fluctuate with technological advancements or policy shifts, and the limited timeframe of the dataset may introduce potential biases, affecting the interpretation of long-term trends. Additionally, while the study aggregates sentiment scores from multiple platforms, it does not provide a comparative analysis across platforms, nor does it differentiate specific AI education applications (e.g., personalized learning, intelligent tutoring, or automated assessment). Although many scholars recognize the value of social media data in the absence of large-scale, high-quality datasets, such data still fail to fully represent all social groups, particularly those with weaker socio-economic and technological capital, thus limiting the generalizability of the findings. The inherent biases in the dataset should therefore be acknowledged as a potential concern. Furthermore, while this study primarily employs quantitative analysis to quantify spatial effects with high explanatory power, it lacks qualitative components (e.g., thematic analysis of social media comments or case studies). Future research could integrate qualitative coding techniques to further investigate the underlying reasons behind specific public sentiments (e.g., concerns about AI ethics). This spatial-statistics-centered approach establishes a foundation for understanding the spatial patterns and evolution of public sentiment towards AI in education. However, these limitations suggest that the interpretation of findings should consider the study's temporal scope, cultural context, and technological application scenarios.