Students’ attitudes and sentiments toward AI-generated images: deep learning-based social media text mining

Recent advancements in Generative Artificial Intelligence (GenAI) have demonstrated its capability to produce creative outputs that closely resemble human creations, particularly in image generation. This technological leap presents significant opportunities and challenges for educational research and practice. To better understand student perceptions of GenAI, this study utilized deep learning-based text mining techniques to analyze 125,952 Weibo posts. The analysis identified six key themes, including application scenarios, personalized expression, technical modeling, prompt engineering, attitudes of educators and institutions, and legal issues. Sentiment analysis revealed that while positive sentiments prevail, negative sentiments fluctuate periodically. Co-occurrence network analysis highlights the sources of negative sentiments, including concerns about spurious images, perceived complexity, employment anxiety, privacy and ethical concerns, and the uncanny valley effect. These findings offer actionable insights for educators, researchers, and policymakers to harness GenAI opportunities.