Understanding and Enhancing Queue Experience for Children in Digital Chats: A Study on AI-powered Interactive Solutions

With the growing use of digital platforms for professional chat support, waiting time before receiving help has become a significant source of frustration and disengagement. This is particularly challenging in digital mental health services for children, where children tend to leave the queue before receiving support. Research indicates that AI-driven interactive elements may help maintain engagement. This thesis investigates how an AI-powered interactive feature can improve the queue experience for children by identifying key design and functional requirements and examining its impact on engagement, boredom, and perceived waiting time. Using a Design Thinking process, two iterations were conducted: the first focused on creating and co-designing a low-fidelity prototype based on prior research, a semi-structured interview with a counselor, and a co-design session with children. The second iteration involved the development of a high-fidelity prototype featuring a large language model (LLM)-based storytelling agent, evaluated with children to assess its impact. Findings highlight the importance of safety boundaries and transparency in designing child-AI systems, while also showing that children value emotional expression, creativity, and personalization in queuing experiences. Additionally, the storytelling conversational agent reduced boredom and perceived waiting time while maintaining engagement. This work contributes to the field of child-AI interaction by presenting design and functional requirements for AI features and by demonstrating the potential of storytelling agents to both engage and emotionally support children during digital chat queue experiences