LunaAI: A Polite and Fair Healthcare Guidance Chatbot

TL;DR

LunaAI is a healthcare chatbot designed with a focus on politeness and fairness, demonstrating improved user trust and interaction quality through ethical conversational principles and user-centered design.

Contribution

This work introduces LunaAI, a healthcare chatbot that emphasizes ethical communication, politeness, and fairness, using a structured design and evaluation approach in healthcare AI.

Findings

LunaAI achieved high user ratings of 4.7/5 for politeness.

LunaAI scored 4.9/5 for fairness in user evaluations.

The system showed measurable improvements over baseline models.

Abstract

Conversational AI has significant potential in the healthcare sector, but many existing systems fall short in emotional intelligence, fairness, and politeness, which are essential for building patient trust. This gap reduces the effectiveness of digital health solutions and can increase user anxiety. This study addresses the challenge of integrating ethical communication principles by designing and evaluating LunaAI, a healthcare chatbot prototype. Using a user-centered design approach informed by a structured literature review, we developed conversational scenarios that handle both routine and hostile user interactions. The system was implemented using the Google Gemini API and deployed as a mobile-first Progressive Web App built with React, Vite, and Firebase. Preliminary user testing was conducted with a small participant group, and responses were evaluated using established frameworks such as the Godspeed Questionnaire. In addition, a comparative analysis was performed between LunaAI's tailored responses and the baseline outputs of an uncustomized large language model. The results indicate measurable improvements in key interaction qualities, with average user ratings of 4.7 out of 5 for politeness and 4.9 out of 5 for fairness. These findings highlight the importance of intentional ethical conversational design for human-computer interaction, particularly in sensitive healthcare contexts.