Generative AI for Personalized Multisensory Immersive Experiences: Challenges and Opportunities for Stress Reduction
Bibliographic record
Abstract
Stress management and relaxation are critical areas of interest in mental health and well-being. Forest bathing is a practice that has been shown to have a positive effect on reducing stress by stimulating all the senses in an immersive nature experience. Since access to nature is not universally available to everyone, virtual reality has emerged as a promising tool to simulate this type of experience. Furthermore, generative artificial intelligence (GenAI) tools offer new opportunities to create highly personalized and immersive experiences that can enhance relaxation and reduce stress. This study explores the potential of personalized multisensory VR environments, designed using GenAI tools, to optimize relaxation and stress relief via two experiments that are currently underway. The first evaluates the effectiveness of non-personalized versus personalized VR scenes generated using AI tools to promote increased relaxation. The second explores the potential benefits of providing the user with additional personalization tools, from adding new virtual elements to the AI-generated scene, to adding AI-generated sounds and scent/haptics customization. Ultimately, this research aims to identify which customizable elements may lead to improved therapeutic benefits for multisensory VR experiences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".