The Therapeutic Potential of AI-Generated Art in Short-Term Stress Management
Bibliographic record
Abstract
Art therapy has been used throughout history to aid in maintaining the mental wellbeing of individuals.With the introduction of generative AI to create artwork from diffusion technique and prompt engineering, we aim to explore the potential benefits of generative AI tools, such as stable diffusion, in helping reduce short-term stress, as well as compare them with traditional art methods.Thirty participants were randomly assigned to one of four condition groups based on the stress-inducing activity (without the stress-inducing activity in a control group and with the stress-inducing activity in an experimental group) and the art activity (AI art generator or traditional sketching task).Stress levels were measured three times throughout the study (before, after the stress-inducing activity, and after the art activity) using a Visual Analogue Stress Scale (VAS).The results of the experiment reveal that, while not as effective as the traditional art method, AI art generators can help reduce short-term stress, which could be attributed to the tool allowing participants to express their thoughts and feelings and experience the feeling of catharsis.i Additionally, I am deeply grateful to my family-my mom, dad, and my brother-for their unwavering
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".