AI Augmented Mascot Design Workflow for Digital Learning Media with Collaborative Intelligence - Jintapitak, Manissaward (Asian Health, Science and Technology Reports, 2026)
Mascot characters are increasingly central to digital communication and learning environments, yet their creation remains dominated by labor-intensive manual workflows. This study investigates whether generative AI can enhance mascot design while preserving coherent character identity. A traditional manual workflow was compared with an AI-assisted collaborative workflow that employed Gemini AI, a structured prompting protocol. This model utilized clear role separation, where AI supported ideation while humans retained identity control and final decision-making. Ten evaluators rated outputs from both workflows on identity coherence, emotional clarity, visual appeal, and variation richness. Results showed that AI assistance substantially increased exploratory breadth and stylistic diversity, yielding significantly higher scores for variation richness and near-significant gains in visual appeal, while identity coherence and emotional clarity remained comparable to the manual condition. Correlation analyses further indicated that greater variation was positively associated with visual appeal. However, it was only weakly related to identity stability, suggesting that AI-generated diversity did not fragment character meaning under human oversight. Overall, the findings support a human-centered collaborative-intelligence framework in which generative AI functions as an exploratory partner rather than a replacement for designers. The proposed workflow offers practical guidance for integrating AI into character and mascot development, with promising implications for branding and educational media.