Generative AI–Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial
Bibliographic record
Abstract
Background: Colleges have turned to digital mental health interventions to meet the increasing mental health treatment needs of their students. Among these, chatbots stand out as artificial intelligence-driven tools capable of engaging in human-like conversations that have demonstrated some effectiveness in reducing depression and anxiety symptoms. Objective: This study aimed to assess the feasibility and acceptability of using Wayhaven, an artificial intelligence chatbot, among college students with elevated depression or anxiety symptoms. We also aimed to examine the preliminary effectiveness of Wayhaven in improving symptoms of anxiety and depression, hopelessness, agency, and self-efficacy among students. Methods: Participants were 50 racially and ethnically diverse college students with elevated depression or anxiety symptoms (n=45, 80% female; mean age 22.12, SD 4.42 years). Students were asked to use Wayhaven over the course of 1 week and completed assessments at preintervention, after 1 session, and 1 week. Results: Wayhaven use was associated with a significant decrease in depression (β=-1.62; P<.001), anxiety (β=-2.15; P<.001), and hopelessness (β=-.64; P<.001) and a significant increase in agency (β=.64; P=.32), self-efficacy (β=.53; P=.02), and well-being (t40=2.90; P=.006; d=0.45) across the study period. Most students also reported being satisfied with Wayhaven and it being a tool they would recommend to their peers. Conclusions: Findings suggest that Wayhaven may be a viable mental wellness resource for diverse students with elevated depression or anxiety symptoms.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".