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 personality assessments with 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. Error structure analysis further revealed systematic assessment biases in LLMs, which multi-context aggregation partially mitigated.

翻译：低侵入性和自动化的人格评估在心理学与人机交互领域日益受到关注。本研究探索了一种基于人格表征多样性的交互式人格评估方法，提出了一个多类人格表征启发的游戏化人格评估框架（Multi-PR GPA）。该框架利用大语言模型为虚拟代理赋予不同人格特征，这些代理通过参与交互式游戏激发人类多维度人格表征。基于交互过程中产生的多类型文本数据，该方法实现了兼具可解释性的人格评估。以经典的大五人格理论为基础，我们开发了原型系统并开展用户研究验证Multi-PR GPA的有效性。结果证实了该方法在人格评估中的有效性，并展示了其在考虑人格表征多样性时的优越性能。误差结构分析进一步揭示了大语言模型中存在的系统性评估偏差，而多情境聚合策略可在一定程度上缓解此类偏差。