PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Qisen Yang, Zekun Wang, Honghui Chen, Shenzhi Wang, Yifan Pu, Xin Gao,, Wenhao Huang, Shiji Song, Gao Huang

TL;DR
PsychoGAT leverages large language models to transform standardized psychological assessments into engaging, personalized interactive fiction games, improving user engagement and psychometric validity.
Contribution
This work introduces PsychoGAT, a novel framework that uses LLMs as both psychologists and game designers to gamify psychological assessments effectively.
Findings
PsychoGAT achieves high reliability and validity in assessments.
Human evaluators rate PsychoGAT's content as coherent and engaging.
PsychoGAT enhances user interest and immersion in psychological testing.
Abstract
Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews, often face challenges with engagement and accessibility. While game-based and LLM-based tools have been explored to improve user interest and automate assessment, they struggle to balance engagement with generalizability. In this work, we propose PsychoGAT (Psychological Game AgenTs) to achieve a generic gamification of psychological assessment. The main insight is that powerful LLMs can function both as adept psychologists and innovative game designers. By incorporating LLM agents into designated roles and carefully managing their interactions, PsychoGAT can transform any standardized scales into personalized and engaging interactive fiction games. To validate the proposed method, we conduct psychometric…
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Taxonomy
TopicsImpact of AI and Big Data on Business and Society · FinTech, Crowdfunding, Digital Finance · Technology Adoption and User Behaviour
