## 4 Discussion
### Distribution Patterns of Sentiment Attitudes  
The study reveals that the public in China generally holds a positive emotional attitude towards the application of AI in education, but this attitude varies significantly across regions. The eastern coastal regions (e.g., Beijing, Guangdong, Shanghai, Zhejiang), characterized by high economic development, advanced information technology, and active technological innovation, exhibit consistently high positive sentiment scores. This result aligns with previous research indicating that economic and technological development levels enhance the acceptance of new technologies [35].  
In the central regions (e.g., Shanxi, Sichuan, Hubei), although the development level is somewhat lower compared to the eastern regions, positive sentiment scores remain relatively high. This could be attributed to the rapid progress in informatization in recent years and increased local government investments in emerging technologies [36]. In contrast, the western regions (e.g., Tibet, Gansu, Qinghai) show lower positive sentiment scores, reflecting not only the inadequacy of economic development and technological dissemination but also potential limitations in public understanding of AI education. These areas' public attitudes towards AI education are likely constrained by imbalanced resource allocation and insufficient technological application scenarios [34; 37].  
The regional differences in sentiment attitudes reflect varying levels of public understanding and discussion of AI education. The spatial distribution of negative sentiment shows a partially overlapping regional pattern with positive sentiment. Despite the dominance of positive sentiment in the eastern regions, certain provinces (e.g., Guangdong, Shanghai) exhibit relatively high levels of negative sentiment. The coexistence of high positive and certain high negative sentiments in the eastern regions indicates that public attitudes towards AI education are not merely supportive or opposed; instead, they reflect both recognition and anticipation of new technology as well as concerns about potential risks, showcasing a multifaceted and developmental approach. In the central and western regions, both positive and negative sentiments are weak, indicating insufficient public understanding and discussion of AI education. Therefore, the lower intensity of sentiment in these regions does not simply reflect negativity but rather a lack of engagement and discourse surrounding AI education compared to the eastern regions.  
The \"high-value clustering\" phenomenon, where high-value areas (e.g., eastern coastal regions) influence surrounding areas, shows the interregional correlation of public sentiment. The hypothesis that public sentiment towards AI education has spatial diffusion properties is confirmed in this study. This diffusion pattern suggests that sentiment attitudes are more likely to resonate regionally through group interactions, reflecting the geographical proximity effect of similar social environments on regional development [38]. Additionally, negative sentiment demonstrates stronger spatial autocorrelation compared to positive sentiment, indicating that negative emotions spread and diffuse more effectively [9]  
Social media platforms play a crucial role in this process. The herd mentality and emotional mimicry mechanisms amplify this effect, as individuals tend to align with and propagate negative views in the face of intense negative discussions within groups. This leads to regional emotional resonance [39; 40]. Social media platforms' algorithms tend to focus on highly discussed topics, and through multi-layered user interactions and information sharing, controversial negative emotions trigger stronger emotional resonance and widespread dissemination [41]. This phenomenon is particularly pronounced when the application of technology is controversial (e.g., ethics, fairness issues), highlighting the need to pay special attention to the risk of negative emotional diffusion when promoting AI education [42].