Artificial Intelligence and Depression: How AI powered chatbots in virtual reality games may reduce anxiety and depression levels
Bibliographic record
Abstract
Depression is a prevailing issue of the 21st Century in dire need of a solution. Trained professionals such as therapists and psychologists are often limited in supply and charge a high price for sessions. This leads to an alternative of using customized AI powered chatbots in full immersion Virtual Reality (VR) games as a substitute for professionals for a consistent and supportive treatment to reduce anxiety and depression levels. However, not much research has been done specifically on AI chatbots in VR games for depression therapy. Therefore, this study is separated into three analyses: analyzing the effects of chatbots on depression, the effects of VR on depression, and the effects of games on depression. Various researches analyzed in this study have supported chatbot therapy to be effective in reducing anxiety levels. VR also provided a platform that can promote concentration and engagement in patients. Analysis of studies on games supported that games provide positive emotions and reduce anxiety. Nevertheless, future primary research must be conducted before reaching a conclusion because of limited data.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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 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".