A Systematic Review on Mental Health Chatbots: Trends, Design Principles, Evaluation Methods, and Future Research Agenda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent decades have witnessed a rise in the prevalence of common mental health problems coupled with an increasing demand for talk‐based psychological therapies. This has coincided with the rise of mental health chatbots (MHCs), a corollary of which is the use of MHCs, AI‐driven, conversational agents, to provide interactive support, guidance, and therapeutic engagement for persons experiencing mental health challenges. There has been a growing research interest in MHCs, and this study provides a much‐needed systematic review that examines this expanding research literature, identifying themes, trends, and areas worthy of further exploration. This review identified and systematically explored 97 published papers on MHCs. Most studies explored MHC design and evaluation, emphasizing empathy‐based chatbots and their relative efficacy compared with conventional delivery modes (e.g., in‐person human therapists). Text‐based communication was the most frequently utilized modality over and above the use of audio, graphical, or mixed modes. The most commonly used therapeutic orientation among MHCs was cognitive behavioral therapy (CBT), with far less focus on third‐wave evidence‐based approaches such as dialectical behavior therapy and acceptance and commitment therapy. The most frequently targeted mental health condition was depression, although there were several other conditions also considered. Most MHC research has not considered cultural appropriateness or cultural adaptation of interventions. Further, limited attention to severe or high‐risk symptomatology has been given. Meanwhile, we noted that most of the studies used quantitative and mixed evaluation methods. Evaluation shows persistent challenges around personalization, privacy, and technical reliability. Future research should focus on integrating human‐in‐the‐loop mechanisms, advancing cultural adaptation, incorporating thought‐challenging CBT techniques, embedding ethics into design, and exploring large language models (LLMs) for more adaptive and empathetic support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it