Balancing Affective Engagement and Cognitive Load in Generative-AI-Based Learning: Empathy, Immersion, and Emotional Design in Design Education

Abstract

As higher education undergoes rapid transformation driven by Artificial Intelligence (AI), the integration of Generative AI (GenAI) has become essential for preparing future-ready creative professionals. In this context, design education plays a leading role in exploring how GenAI can enhance students’ experiential learning. This study empirically examined how three experience dimensions—Educational, Entertainment, and Aesthetic—shape Empathy, Immersion, Satisfaction, and Learning Outcomes in a GenAI-based self-character workshop. A total of 185 design students participated, and the data were analyzed using Structural Equation Modeling (SEM). The results revealed that both Entertainment (β = 0.334, p < 0.001) and Aesthetic (β = 0.434, p < 0.001) experiences significantly and positively predicted Empathy and also increased Immersion (β = 0.215, p < 0.001; β = 0.154, p < 0.05). In contrast, Educational experience showed a non-significant or slightly negative effect. Furthermore, Empathy enhanced Immersion (β = 0.220, p < 0.01), Satisfaction (β = 0.173, p < 0.05), and Learning Outcomes (β = 0.305, p < 0.001). Immersion also improved Learning Outcomes (β = 0.253, p < 0.05) but slightly reduced short-term Satisfaction (β = −0.186, p < 0.05), indicating a cognitive-load trade-off between concentration and immediate enjoyment. These findings demonstrate that GenAI-based creative activities can effectively foster both emotional engagement and learning performance when instructional design minimizes unnecessary cognitive burden. The study contributes to understanding how emotionally meaningful and aesthetically engaging experiences can advance AI-integrated design education in the digital transformation era.