ISSN 0439-755X
CN 11-1911/B

Acta Psychologica Sinica ›› 2025, Vol. 57 ›› Issue (11): 2022-2042.doi: 10.3724/SP.J.1041.2025.2022

• Reports of Empirical Studies • Previous Articles     Next Articles

Self-help AI psychological counseling system based on large language models and its effectiveness evaluation

HUANG Feng1,2,3, DING Huimin4,5, LI Sijia6, HAN Nuo7,8, DI Yazheng1,2, LIU Xiaoqian1,2, ZHAO Nan1,2, LI Linyan3,9, ZHU Tingshao1,2()   

  1. 1State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
    2Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
    3Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong SAR 999077, China
    4School of Education, Renmin University of China, Beijing 100872, China
    5Department of Psychology, University of Notre Dame, IN 46556, USA
    6Department of Social Work and Social Administration, Faculty of Social Sciences, University of Hong Kong, Hong Kong SAR 999077, China
    7Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai 519087, China
    8Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
    9Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR 999077, China
  • Received:2024-08-15 Published:2025-11-25 Online:2025-09-25
  • Contact: ZHU Tingshao, E-mail: tszhu@psych.ac.cn
  • Supported by:
    Beijing Municipal Natural Science Foundation(IS23088)

Abstract:

This study aimed to explore the technical feasibility of constructing a self-help AI psychological counseling system based on large language models without relying on real case data, and to evaluate its effectiveness in improving mental health outcomes in general populations. The research was conducted in two phases: First, we developed a self-help AI psychological counseling chatbot system using zero-shot learning and chain-of-thought prompting strategies; Subsequently, we evaluated the system's practical effectiveness through a two-week randomized controlled trial with 202 participants. Results from Experiment 1 demonstrated that the GPT-4o model, after prompt engineering optimization, showed significant improvements in Compliance, Professionalism, Emotional Understanding and Empathy, as well as Consistency and Coherence. Experiment 2 revealed that compared to the control group, participants using the self-help AI psychological counseling chatbot experienced significant short-term improvements in depression, anxiety, and loneliness. Notably, anthropomorphized AI counselors demonstrated significant advantages in alleviating loneliness, while non-anthropomorphized designs were more effective in reducing stress. Additionally, improvements in anxiety symptoms persisted at one-week follow-up, while improvements in other indicators did not sustain. This study preliminarily explores the positive impact of LLM-based self-help AI psychological counseling on mental health, revealing differential effects of various AI designs on specific psychological issues, and provides valuable insights for future research and practice.

Key words: artificial intelligence, large language models, chain-of-thought, mental health, self-help psychological counseling, randomized controlled trial