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PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents

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arxiv 2402.12326 v2 pith:H5DJALHE submitted 2024-02-19 cs.CL cs.CYcs.HCcs.LGcs.MA

classification cs.CLcs.CYcs.HCcs.LGcs.MA
keywords psychogatpsychologicalagentsassessmentcontentengagementevaluationsfiction
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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 evaluations to assess its effectiveness and employ human evaluators to examine the generated content across various psychological constructs, including depression, cognitive distortions, and personality traits. Results demonstrate that PsychoGAT serves as an effective assessment tool, achieving statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity. Moreover, human evaluations confirm PsychoGAT's enhancements in content coherence, interactivity, interest, immersion, and satisfaction.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Odyssey of the Fittest: Can Agents Survive and Still Be Good?

    cs.AI 2025-02 reject novelty 6.0 of 10

    In an LLM-generated text survival game, a GPT-4o agent was reported to survive better and score more ethically than NEAT and SVI Bayesian agents, but the evaluation is circular because GPT-4o labels its own behavior.

  2. BlossomPsy: A User-Centric AI System for Adaptive and Engaging MBTI Personality Assessments

    cs.HC 2026-07 conditional novelty 5.0 of 10

    BlossomPsy combines multi-turn LLM dialogue, photo-based questions, a multi-head classifier, and a modified UCB bandit algorithm to deliver MBTI assessments with higher user engagement and preliminary consistency with...

  3. Evaluation and Benchmarking of LLM Agents: A Survey

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A review that proposes a two-dimensional taxonomy for evaluating LLM agents and highlights enterprise-specific evaluation gaps.

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