An intelligent agent for sentence completion test: creation and application in depression assessment
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 advan...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1649905/full |
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| author | Yuchen Huang Yuchen Huang Mengxiao Lei Hanyu Zhang Lu Zong Bin Zhu Hong Luo |
| author_facet | Yuchen Huang Yuchen Huang Mengxiao Lei Hanyu Zhang Lu Zong Bin Zhu Hong Luo |
| author_sort | Yuchen Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-99054c6ecce04157b4b9a1de9edd5133 |
| institution | Kabale University |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-99054c6ecce04157b4b9a1de9edd51332025-08-20T04:02:41ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-08-011610.3389/fpsyg.2025.16499051649905An intelligent agent for sentence completion test: creation and application in depression assessmentYuchen Huang0Yuchen Huang1Mengxiao Lei2Hanyu Zhang3Lu Zong4Bin Zhu5Hong Luo6Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaHangzhou PsychSnail Technology Company Limited, Hangzhou, ChinaHangzhou PsychSnail Technology Company Limited, Hangzhou, ChinaHangzhou Yunqi Interdisciplinary Technology Research Institute, Hangzhou, ChinaSuzhou Guangzhinian Technology Company Limited, Suzhou, ChinaSchool of Journalism & Communication, Hangzhou City University, Hangzhou, ChinaAffiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDuring 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1649905/fullsentence completion testprojective testlarge language modelsAI agentdepression assessment |
| spellingShingle | Yuchen Huang Yuchen Huang Mengxiao Lei Hanyu Zhang Lu Zong Bin Zhu Hong Luo An intelligent agent for sentence completion test: creation and application in depression assessment Frontiers in Psychology sentence completion test projective test large language models AI agent depression assessment |
| title | An intelligent agent for sentence completion test: creation and application in depression assessment |
| title_full | An intelligent agent for sentence completion test: creation and application in depression assessment |
| title_fullStr | An intelligent agent for sentence completion test: creation and application in depression assessment |
| title_full_unstemmed | An intelligent agent for sentence completion test: creation and application in depression assessment |
| title_short | An intelligent agent for sentence completion test: creation and application in depression assessment |
| title_sort | intelligent agent for sentence completion test creation and application in depression assessment |
| topic | sentence completion test projective test large language models AI agent depression assessment |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1649905/full |
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