ORIGINAL RESEARCH article
Front. Psychol.
Sec. Quantitative Psychology and Measurement
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1649905
An Intelligent Agent for Sentence Completion Test: Creation and Application in Depression Assessment
Provisionally accepted
- 1Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
- 2Hangzhou PsychSnail Technology Company Limited, Hangzhou, China
- 3Hangzhou Yunqi Interdisciplinary Technology Research Institute, Hangzhou, Zhejiang, China, Hangzhou, China
- 4Suzhou Guangzhinian Technology Company Limited, Suzhou, Jiangsu, China, Suzhou, China
- 5Hangzhou City University, Hangzhou, China
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During large-scale psychological screening, traditional self-report questionnaires face challenges like response deception or social desirability bias, while the Sentence Completion Test (SCT) as a projective technique shows potential but is limited by manual scoring and high costs. Leveraging advancements in Large Language Models (LLMs), this study integrates SCT's theoretical framework with LLM capabilities to develop a specialized set of SCT items for depression assessment in Chinese university students, using a self-built intelligent agent across three progressive empirical studies. Results show the agent demonstrates good reliability (Cronbach's α=0.89-0.92) and validity, with high consistency to manual scoring (r=0.96), significant criterion correlations with the Beck Depression Inventory (r=0.89) and Self-Rating Depression Scale (r=0.85), confirmed structural validity via exploratory factor analysis. Furthermore, the intelligent agent could identify most invalid responses (F1 = 0.94, Accuracy = 0.99, Precision = 0.99, Recall = 0.90). This research marks a key milestone in SCT's intelligent transformation, driving innovation in psychological assessment and offering new academic and practical pathways.
Keywords: Sentence Completion Test, Projective test, Large language models, AI Agent, Depression assessment
Received: 19 Jun 2025; Accepted: 23 Jul 2025.
Copyright: © 2025 Huang, Lei, Zhang, Zong, Zhu and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Bin Zhu, Hangzhou City University, Hangzhou, China
Hong Luo, Zhejiang University School of Medicine Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Hangzhou, China
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