Judging the Reasons for Generating Artificial Intelligence Paintings in Human-Computer Interaction: Topic Analysis Based on Natural Language Processing

Studying the interaction between people and generative AI art has become a popular direction in interdisciplinary studies: this study explores the key mechanisms in people's cognitive responses when identifying generative AI paintings through natural language processing (NLP) techniques. By analyzing the textual feedback of 17 Chinese college students with art appreciation backgrounds, we adopt topic modeling and sentiment analysis to explain their perceptual criteria and emotional responses. This study reveals two key contributions: first, it finds the "paradoxical authenticity" of AI-generated artworks in the eyes of students. Second, it reveals that people tend to reinterpret human imperfections ,such as brushstroke tremors and color imbalances, as signs of artistic authenticity. These findings promote the discussion of creative coexistence between humans and AI and provide empirical insights into human-computer interaction and dynamic evaluation systems for generative art.