Creators’ perceptions and attitudes toward using generative artificial intelligence: Exploring posts and comments related to AIGC design learning on a Chinese social media platform with a mixed-method approach

Article ID: 1624

Abstract

Artificial intelligence generated content (AIGC) has been found to play a crucial role in the field of design, where its significance in various creative clusters has been increasingly recognized. However, the lack of in-depth understanding of creators’ AIGC learning experiences and sentiments in the design area has become an obstacle to the further development of targeted educational programs and industry-relevant initiatives. It remains unclear what specific clusters of AIGC design learning creators are focusing on and what kinds of attitudes, positive or negative, they hold towards these different clusters within the field. To bridge these gaps, this study collected 9992 posts and comments related to AIGC design learning on a Chinese social media platform called Xiaohongshu. A mixed-method approach was applied by combining word cloud, sentiment analysis, co-word analysis, and social network analysis. Using word cloud and sentiment analysis, this research aimed to uncover the key perceptions and sentiment orientations creators focused on in their expression. Social network analysis and co-word matrix were used to identify central concepts, their connections and clusters of related terms. The result differentiates creators’ perceptions into the following clusters: tools and technical foundations for AIGC design, application domains of AIGC design, cultural elements used in AIGC, semantic nuances in AIGC terminology, AIGC design methods, creativity and innovation in AIGC design, future-oriented perspectives in AIGC design for professional development, and AIGC design ethics. Moreover, it presents the tendencies and proportions of creators’ attitudes in the above clusters in four main sentiment categories: positive, moderately positive, moderately negative, and negative. This study is expected to have implications in providing practical guidance for educators to optimize AIGC design teaching strategies, thereby better meeting learners’ emotional needs and increasing their willingness to learn AIGC design knowledge, as well as helping the industry develop better AIGC-designed products.