| Category |
Psychology |
| Manuscript Type |
Prospective Study |
| Article Title |
Feasibility of human-in-the-loop multimodal generative artificial intelligence chatbot for school-based adolescent mental health support
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xi-Wang Fan, Ting-Yu Lin, Ming-Hao Wang, Li-Cheng Wu, Xin-Xi Chen, Jun-Yao Chen, Yi-Zhen Wu, Yu Zheng, Tang Li, Hao Du, Yao-Xuan Wang, Yu-Jie Wang, Jing-Wen Zhang, Li-Wei Huang, Tian-Ze Fan, Bing Liu, Jie Liu, Pei Sun, Hui Zhao, Guo-Lin Ma, Qiang Cheng and Shi-Jun Li |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the Beijing Nova Programme Interdisciplinary Cooperation Project |
20240484674 |
| Capital’s Funds for Health Improvement and Research |
CFH2024-2-5024 |
| the Tongji University Medicine-X Interdisciplinary Research Initiative |
2025-0708-YB-02 |
|
| Corresponding Author |
Shi-Jun Li, Department of Radiology, First Medical Center of Chinese PLA General Hospital, /, Beijing 100853, China. shijunli07@yeah.net |
| Key Words |
Generative artificial intelligence; Adolescent mental health; Chatbot; Feasibility study; Depression; Nonrandomized pilot study; Quasi-experimental study; Acceptance and commitment therapy; Safety monitoring; Human-in-the-loop |
| Core Tip |
This pilot study evaluated the supervised school-based implementation of Duoduo, an acceptance and commitment therapy-informed multimodal generative artificial intelligence chatbot developed for adolescents with elevated depressive symptoms. Most participants completed the 2-week intervention, engaged with the system for an average total duration of approximately 101 minutes, and reported a moderate working alliance. Risk-related conversation signals were referred for staff review through a predefined human-in-the-loop safety workflow. The findings primarily support the feasibility of implementation and provide guidance for future program planning. Clinical outcome findings were considered exploratory because participant allocation was pragmatic, baseline symptom severity differed between groups, multiple outcomes were evaluated, and the follow-up period was brief. |
| Citation |
Fan XW, Lin TY, Wang MH, Wu LC, Chen XX, Chen JY, Wu YZ, Zheng Y, Li T, Du H, Wang YX, Wang YJ, Zhang JW, Huang LW, Fan TZ, Liu B, Liu J, Sun P, Zhao H, Ma GL, Cheng Q, Li SJ. Feasibility of human-in-the-loop multimodal generative artificial intelligence chatbot for school-based adolescent mental health support. World J Psychiatry 2026; In press
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