Deploying a Mental Health Chatbot in Higher Education: The Development and Evaluation of Luna, an AI-Based Mental Health Support System
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
Rising mental health challenges among postsecondary students have increased the demand for scalable, ethical solutions. This paper presents the design, development, and safety evaluation of Luna, a GPT-4-based mental health chatbot. Built using a modular PHP architecture, Luna integrates multi-layered prompt engineering, safety guardrails, and referral logic. The Institutional Review Board (IRB) at the University of Detroit Mercy (Protocol #23-24-38) reviewed the proposed study and deferred full human subject approval, requesting technical validation prior to deployment. In response, we conducted a pilot test with a variety of users—including clinicians and students who simulated at-risk student scenarios. Results indicated that 96% of expert interactions were deemed safe, and 90.4% of prompts were considered useful. This paper describes Luna’s architecture, prompt strategy, and expert feedback, concluding with recommendations for future human research trials.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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