ServeSmart: Bridging Theory and Practice in Customer Service Education through AI-Supported Experiential Learning

Artificial Intelligence (AI) is increasingly used in higher education to support experiential learning in customer service training, where applying theoretical frameworks such as the SERVQUAL model can be challenging. Students frequently struggle with emotional recognition, immediate decision-making, and applying SERVQUAL dimensions during role-play. To address these gaps, this study introduces ServeSmart, an AI-supported learning system that provides real-time guidance during customer service scenario role-play. Using a preliminary exploratory quantitative survey design, this study evaluated students' perceived learning experiences and the platform's usability. Using a non-probability purposive sampling strategy, 30 undergraduate students participated in individual scenario-based simulations followed by a validated post-evaluation questionnaire. Baseline findings indicated that a majority of participants had difficulty with emotional recognition and lacked immediate instructional feedback during simulations. Post-simulation results revealed that all participants reported higher perceived confidence in executing customer service tasks, a clearer perceived understanding of SERVQUAL concepts, and strong interest in future platform utilization. Overall, the findings demonstrate positive student perceptions of ServeSmart as a supportive experiential learning tool, highlighting its potential to offer an immediate instructional platform in service management education.