AI 공감을 활용한 콜센터 감정노동 지원 서비스 디자인: 사용자 참여 디자인을 중심으로

Background Call center employees significantly influence corporate image and customer satisfaction through their interactions but face considerable emotional labor, which negatively impacts their mental and physical health and organizational productivity. While artificial intelligence(AI) technology has been introduced in many call centers to improve efficiency, the issue of emotional labor remains unresolved, limiting the perceived benefits. Given the close connection between work efficiency and psychological stress, enhancing the psychological stability and work environment of call center employees is crucial for both their well-being and organizational efficiency. This study explores how AI technology can alleviate the emotional labor of call center employees by identifying their practical needs and designing a prototype for an AI empathy-based emotional labor support service grounded in cognitive empathy. Methods his study utilized the theoretical framework from previous research(Na et al., 2024) to design an AI empathy-based emotional labor support service aimed at reducing the emotional burden on call center employees. Initial user requirements were gathered through a participatory design workshop, with data analyzed using affinity diagrams to inform the prototype design. After a second round of user research, feedback was incorporated to refine the final service. Results The design workshop identified several pain points: the need for overtime to summarize and organize consultation content, difficulties in conducting smooth consultations due to limited expertise and authority, and psychological stress from difficult customers. To address these issues, a prototype was developed with features such as ‘customer history information,’ ‘call time visualization,’ ‘customer voice decibel detection,’ and ‘AI-based consultation post-processing.’ User feedback from the second round of research indicated that the AI empathy-based emotional labor support service effectively reduced emotional labor and increased work efficiency. Features like ‘real-time consultation summary,’ ‘automatic recording of consultation content,’ and ‘keyword detection’ were particularly valued. Conclusions This study holds academic significance in confirming that continuously gathering and incorporating the needs, pain points, and design feedback of call center employees play a crucial role in improving service quality. The study also has practical implications by enhancing design effectiveness based on user demands, maximizing user satisfaction, and expanding the applicability of AI empathy technology. By developing a workshop toolkit derived from theoretically identified AI solutions, the research reflects the unique context of call centers. Despite limitations such as workshop constraints, participant variability, and AI technology maturity, AI empathy-based emotional labor support services can improve employees’ mental and physical well-being while enhancing organizational productivity and efficiency.