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Breaking Barriers in Student Mental Health Care With AI-Enhanced Group Cognitive Behavioral Therapy: Pilot
Bogdan Tudor Tulbure1, Bianca Spătaru2, Andrei Rusu1
1Department of Psychology, West University of Timisoara, 4 Vasile Parvan Bvd, Timisoara, 300223, Romania, 40 745-753-061.
JMIR Formative Research
|June 12, 2026
Summary
This pilot study found that a large language model (LLM) chatbot, used alongside group therapy, is a feasible and acceptable way to support university students with anxiety and depression symptoms. The chatbot-assisted therapy showed promising results for improving mental well-being.
Area of Science:
- Mental Health Technology
- Digital Psychiatry
- Clinical Psychology
Background:
- University students face significant psychological distress and limited mental health support.
- Cognitive Behavioral Therapy (CBT) is effective but faces access barriers, highlighting the need for scalable solutions.
- Large Language Model (LLM) chatbots offer potential for enhancing CBT delivery and support.
Purpose of the Study:
- To evaluate the feasibility, acceptability, and preliminary efficacy of an LLM chatbot as adjunct support for group Unified Protocol (UP) therapy.
- To assess the chatbot's role in providing between-session support for university students with subclinical anxiety and depression.
Main Methods:
- A single-arm pilot study involving university students with subclinical anxiety/depression symptoms.
- Intervention: 4 weekly group UP sessions with an LLM chatbot for between-session support.
- Outcomes: Feasibility (adherence, engagement, usability), preliminary efficacy (anxiety, depression, well-being), and qualitative feedback.
Main Results:
- High feasibility and acceptability observed, with excellent system usability (mean 84.94/100).
- Significant improvements in generalized anxiety (P=.004) and mental well-being (P=.02) were noted.
- Substantial chatbot engagement and high treatment retention (10.5% dropout rate) were achieved.
Conclusions:
- Augmenting group UP therapy with an LLM chatbot is feasible, acceptable, and shows preliminary efficacy for students.
- The hybrid approach demonstrates strong retention, engagement, and safety, supporting further research.
- Findings suggest this technology-enhanced intervention could expand access to evidence-based mental health care.
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