抄録
Generative AI has increasingly become an essential tool in game art creation; however, significant gaps persist between students and professional artists in workflow practices, prompt design, and image control. This exploratory study conducts a comparative analysis between case examples drawn from student experiments and publicly documented professional workflows. Results reveal that students typically rely on intuitive trial and error prompting often yielding inconsistent or sub-optimal outputs whereas professionals employ structured prompt engineering and systematic refinement to ensure image quality and stylistic coherence. In response, we propose a lightweight educational framework comprising a Structured Prompting Template and an Iterative Generation Cycle scaffold. Preliminary simulation exercises demonstrate that these tools significantly enhance students’ prompt coherence, visual consistency, and critical evaluative engagement. This paper provides foundational guidance for integrating generative AI into game art education and suggests avenues for future pedagogical development across diverse visual domains.
