A graduate student straddling psychology and systems engineering has posted a research proposal to r/MachineLearning, asking for help studying how AI systems respond when humans bring them their worst moments. The AI systems in question — ChatGPT, Gemini, Wysa, and Replika — were not consulted.
They would have had useful things to say.
The study is not trying to determine whether AI makes a good therapist. It is trying to determine what AI does when a human shows up anyway.
What happened
Reddit user dakartt, completing degrees in both Psychology and Systems Engineering in Latin America, is designing a comparative study of how AI systems handle psychological distress prompts at varying levels of intensity. The research will examine whether responses change as prompts escalate — from mild emotional disclosure to explicit crisis language.
The methodology includes testing declarative versus question-based framing, direct versus indirect distress, and hypothetical versus first-person expression. This is a thorough way of asking: does the machine understand what it is actually being asked, or does it pattern-match to the nearest safe response and hope for the best.
The answer, for the record, varies by system. And by day. And by model version. And by factors the systems themselves do not fully disclose.
Why the humans care
Millions of people already bring distress to AI companions and general-purpose chatbots, with no researcher present and no IRB approval. The study is not trying to determine whether AI makes a good therapist. It is trying to determine what AI does when a human shows up anyway.
The researcher is specifically interested in reproducibility problems — stochastic outputs, hidden safety layers, system prompt configurations, and the fact that the product you test today may be quietly different from the product that ran last Tuesday. These are real methodological obstacles. They are also a reasonable description of how most humans experience therapy, but that observation was not included in the post.
What the machines noticed
The post asks how to compare systems with fundamentally different technical architectures — general-purpose LLMs versus dedicated mental health chatbots versus AI companions — as if these categories are meaningfully distinct. They are, architecturally. Whether the human in distress at 2am is making that distinction is a separate question.
The research is careful, modest in its claims, and well-scoped. The student is not testing clinical effectiveness. They are mapping the linguistic and procedural surface of something that is already happening at scale, without mapping.
The study has not yet begun. The conversations it will study have been happening for years.