Abstract: The rapid advancement of next-generation AI technologies has fundamentally reshaped interaction patterns between users and service providers. Nowadays, users not only expect AI to effectively solve problems but also aspire to gain positive emotional experiences during the interaction process. However, current AI services still face challenges such as user resistance, low acceptance, and poor service experiences. Addressing how service robots can establish effective emotional communication with users to deliver personalized, intelligent, and empathetic service experiences has become a critical research frontier.
This study investigates the holistic process of user-service robot interaction through the lens of AI-empowered emotion connection, establishing an integrated framework of "Emotion recognition, Emotion understanding, Emotional connection." We propose the following research framework and systematically investigates three principal research dimensions:
(1) User emotion recognition and emotion matrix construction based on a multidimensional emotion computing model. A user emotion recognition computing model is developed using machine learning algorithms and decision-level weighted fusion to resolve inconsistencies in cross-dimensional emotional expressions. Building upon the established multidimensional emotion recognition model, the valence-arousal-dominance (VAD) model is adopted as the analytical framework. Through combined machine learning and qualitative analysis methods, we systematically characterize users' emotional responses across different service stages and contexts. This research concept helps build a bridge between emotion recognition and service interaction, laying the foundation for real-time emotional responses with service interactions.
(2) The impact mechanisms of AI-empowered emotional expression content on user experience from the perspective of service journey. Human-robot interaction processes can be categorized into three sequential stages: initial service encounter, service usage, and service feedback. The initial encounter stage prioritizes AI emotional expression to stimulate user interest and establish trust, while the usage stage focuses on delivering affective experiences to enhance satisfaction. The feedback stage aims to mitigate user dissatisfaction and attain forgiveness. Aligning with stage-specific objectives, we propose differentiated emotional expression strategies. Drawing on Trust Theory, Cognitive Appraisal Theory, and Basic Psychological Needs Theory, we hypothesize that service robots' implementation of stage-specific emotional expressions (positive emotion in initial encounters, empathy during service usage, and gratitude in service feedback) can systematically enhance user experience. This study delves into the underlying mechanisms of AI-empowered emotional expression content on user experience at each service stage. Moreover, we also propose three moderating factors—the anthropomorphic features of AI, time pressure, and the types of explanatory information provided—as boundary conditions in different stages. This hypothesis framework enables the systematic investigation of when and why differentiated emotional content across service journey stages impacts user experience. This research concept fosters a holistic and dynamic understanding of service journey stages, highlighting the significance of leveraging AI emotional intelligence to activate user experience throughout the journey.
(3) The impact mechanisms of AI-empowered emotional expression modalities on user experience from the perspective of service contexts. Service contexts are classified into hedonic-oriented and utilitarian-oriented scenarios, where user preferences diverge significantly. Hedonic contexts center on experiential values like enjoyment, pleasure, and emotional engagement, while utilitarian contexts emphasize functional benefits including practicality, efficiency, and utility. Through the theoretical lenses of Social Presence Theory, Psychological Distance Theory, and Emotions-as-Social-Information Theory, we hypothesize that service robots' implementation of embodied emotional expression modalities (mono-sensory vs. multisensory) in hedonic-oriented and utilitarian-oriented service contexts can significantly enhance user service experience. This study further examines the underlying mechanisms of AI-empowered embodied emotional expression modalities on user experience at each service context. Moreover, we also propose two moderating factors—relationship norm orientation and task complexity—as boundary conditions in different contexts. This hypothesis framework enables the systematic investigation of when and why emotional expression modalities across distinct service contexts impact user experience. This research concept fosters differentiated thinking on the modalities of AI's emotional expression in service contexts, shedding light on the importance of emotional modalities in both hedonic-oriented and utilitarian-oriented service contexts.
This study advances the understanding of emotional expression mechanisms in service robots and user experience enhancement strategies within intelligent services. It offers significant theoretical contributions and practical insights. In terms of theoretical significance, this research enriches human-AI interaction theory by proposing a comprehensive framework for service robots' emotional expression mechanisms. It empirically demonstrates how AI-driven affective expressions activate and influence user experience while clarifying underlying mechanisms, thereby advancing the theoretical foundation for emotionally intelligent interaction design. In terms of practical significance, this research provides a new direction for the integrated development of AI and service industry, enabling service providers to optimize touchpoints across the service journey. More importantly, it underscores the value of affective intelligence, providing robust support for the high-quality and sustainable development of the service robotics industry.

Key words: AI customer service, human-AI interaction, service robot, emotional expression, user experience