EmoEden: Applying Generative Artificial Intelligence to Emotional Learning for Children with High-Function Autism
Yilin Tang, Liuqing Chen, Z Chen, Wenkai Chen, Yu Cai, Yao Du, Fan Yang, Lingyun Sun
Abstract
Children with high-functioning autism (HFA) often face challenges in emotional recognition and expression, leading to emotional distress and social difficulties. Conversational agents developed for HFA children in previous studies show limitations in children's learning effectiveness due to the conversational agents’ inability to dynamically generate personalized and contextual content. Recent advanced generative Artificial Intelligence techniques, with the capability to generate substantial diverse and high-quality texts and visual content, offer an opportunity for personalized assistance in emotional learning for HFA children. Based on the findings of our formative study, we integrated large language models and text-to-image models to develop a tool named EmoEden supporting children with HFA. Over a 22-day study involving six HFA children, it is observed that EmoEden effectively engaged children and improved their emotional recognition and expression abilities. Additionally, we identified the advantages and potential risks of applying generative AI to assist HFA children in emotional learning.