Comparing Human and AI Therapists in Behavioral Activation for Depression: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: Large language models (LLMs) have rapidly advanced across numerous fields, including mental health care. A shortage of trained therapists and mental health care providers has driven informal use of LLMs for therapeutic support. However, their clinical utility remains poorly defined. Objective: This study aimed to systematically evaluate and compare the therapeutic knowledge and single-turn response capabilities of LLMs versus psychotherapists in training in the context of behavioral activation (BA) therapy for depression, and to assess how both groups' performance changed when provided with structured therapeutic training materials. Methods: Six LLMs and 8 human participants completed a questionnaire on depression and BA with 20 multiple-choice items and 10 therapy scenarios, each with 3 open-ended items, that postulated empathic response, use of validation strategies, and theory of mind capabilities. Human participants completed the questionnaire before and after a 5-hour workshop and 5-week period with learning materials. The LLMs received identical training content as context during the second test. All open-ended questions were rated on 5-point scales by 2 experts. Results: At baseline, the LLMs demonstrated higher knowledge scores than human participants (61.0 vs 52.0 out of 100 points) and were rated higher in empathy (U=2.0; P=.005; r=0.917), validation quality (U=2.5; P=.006; r=0.896), anticipation of cognition (U=0.0; P=.002; r=1.000), and anticipation of emotion (U=0.0; P=.002; r=1.000). Following BA training, the LLMs maintained their performance advantage across multiple-choice and open-ended items. Conclusions: The results suggest that LLMs may generate high-quality therapeutic single-turn responses that integrate clinical knowledge with empathetic communication. The findings hint at LLMs' potential as valuable tools in mental health care, although further clinical trials are needed to evaluate their performance in ongoing therapeutic relationships and clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".