Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities
Baiqiao Zhang, Xiangxian Li, Chao Zhou, Xinyu Gai, Juan Liu, Xue Yang, Xiaojuan Ma, Yong-jin Liu, Yulong Bian
Published: 2025/7/5
Abstract
The low-intrusion and automated personality assessment is receiving increasing attention in psychology and human-computer interaction fields. This study explores an interactive approach for personality assessment, focusing on the multiplicity of personality representation. We propose a framework of Gamified Personality Assessment through Multi-Personality Representations (Multi-PR GPA). The framework leverages Large Language Models to empower virtual agents with different personalities. These agents elicit multifaceted human personality representations through engaging in interactive games. Drawing upon the multi-type textual data generated throughout the interaction, it achieves two modes of personality assessment (i.e., Direct Assessment and Questionnaire-based Assessment) and provides interpretable insights. Grounded in the classic Big Five personality theory, we developed a prototype system and conducted a user study to evaluate the efficacy of Multi-PR GPA. The results affirm the effectiveness of our approach in personality assessment and demonstrate its superior performance when considering the multiplicity of personality representation.