Extending the horizon by empowering government customer service officers with ACQAR for enhanced citizen service delivery

• Main Authors: LEE, Hui Shan, SHANKARARAMAN, Venky, OUH, Eng Lieh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Citizen Service Delivery; Question-Answer Model; Empath; Service Level Agreement; ChatGPT; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8649; https://ink.library.smu.edu.sg/context/sis_research/article/9652/viewcontent/ExtendingHorizonGovernmentCSOs_ACQAR_av.pdf
• Institution: Singapore Management University

A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath X SLA predictor and CQAS (a type of Question Answer model) as critical inputs to ChatGPT engine, known as AI Based Citizen Question-Answer Recommender (ACQAR).Empath X SLA predictor anticipates the expected service response time based on citizens’ emotional state. These valuable inputs coupled with the recommended answer provided by the CQAS will serve as prompt inputs to ChatGPT to craft contextually aware responses. This ensures that the final response considers the citizen’s emotional needs, expected service timeline, and recommended answers from official government documents.While the full-scale deployment of this pilot system, ACQAR, is pending, this paper presents a comprehensive blueprint for governments seeking to modernize citizen service delivery. By fusing sentiment analysis, SLA prediction, question-answer models, and ChatGPT, this system design aims to revolutionize government-citizen interactions, delivering more empathetic, efficient, and tailored responses, while not violating SLA.This paper serves as a foundational step towards the practical development and implementation of an intelligent system (ACQAR) that holds the potential to significantly enhance citizen satisfaction, foster trust in government services, and strengthen overall government-citizen relationships.