37 OBSERVATION ON THE AUDIENCE PSYCHOLOGY OF THE AI SPECIAL EXHIBITION IN NATURAL HISTORY MUSEUMS FROM THE PERSPECTIVE OF BEHAVIORAL AND COGNITIVE SCIENCE
Bibliographic record
Abstract
Introduction: Natural history museums are unique educational and cultural institutions that translate complex scientific knowledge into immersive, tangible experiences. Beyond their traditional educational functions, these museums can also promote psychological well-being. This study explores how artificial intelligence (AI)–enhanced special exhibitions contribute to both cognitive development and mental health, including emotional regulation, stress reduction, anxiety management, and empathy enhancement. By integrating perspectives from cognitive psychology, affective neuroscience, and museum education, the paper aims to demonstrate that AI-mediated environments can serve as informal yet effective interventions for improving emotional resilience and social connection. Methods: The study synthesizes interdisciplinary evidence from behavioral and cognitive sciences, cultural psychiatry, and human–computer interaction. It examines three representative institutions—the Canadian Museum of Nature (CMN), the Insectarium de Montréal (IDM), and the Ningbo Choulo Insect Museum (NCIM)—each of which has implemented distinctive AI technologies such as virtual reality (VR), augmented reality (AR), affective computing, and gamified adaptive systems. These cases are analyzed with respect to cognitive processes (attention, memory, flexibility), emotional mechanisms (stress regulation, anxiety modulation, positive affect), and social dimensions (empathy, cooperation, inclusion). Quantitative and qualitative data from existing reports, pilot studies, and visitor analytics are synthesized to evaluate learning outcomes and affective effects. Results: AI-enhanced exhibitions significantly improve visitor engagement, attention, and memory consolidation. More importantly, they support emotional balance and mental health by promoting relaxation, curiosity, and self-efficacy. Physiological and observational indicators suggest reductions in anxiety and stress responses, especially in bioresponsive environments that adapt content to visitors’ arousal levels. Multisensory interactions and perspective-taking tasks enhance empathy, reduce emotional blunting, and encourage pro-social behavior. Cross-cultural comparisons among Canada, Quebec, and China reveal that emotional expression, anxiety coping, and attention styles differ by context, underscoring the importance of adaptive and inclusive AI design. Conclusions: AI-enhanced natural history museums represent emerging public spaces where education, emotion, and technology converge to support collective well-being. By combining immersive design with psychological insights, these institutions not only foster curiosity and scientific literacy but also contribute to emotional stability, resilience, and mental health promotion. Integrating AI-based affective systems and behavioral analytics within museum settings holds promise for scalable, community-centered interventions that address stress, anxiety, and emotional disorders while strengthening empathy and social cohesion.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".