Artificial Intelligence for Eco-Didactic Installations through Interactive Museological Experience to Encourage Sustainable Action
Bibliographic record
Abstract
Interactivity has demonstrated significant efficiency in enhancing the informal learning experience. Environmental museums offer a diverse range of interactive experiences, encompassing both digital and analog mediums, to educate visitors about the environment and sustainability. Studies show that Artificial Intelligence (AI) technologies have enriched digital interactivity by incorporating features such as real-time data processing, object recognition, and personalized recommendations. The implementation of these technologies in public spaces, such as museums, has started and rapidly developing. Given these, the study aims to establish potential alliances between museological learning, interactivity, and AI to amplify the impact of environmental learning experiences in the public space. The integration of AI and interactivity has the potential to foster effective learning experiences, ultimately leading to behavioral changes toward sustainable practices. This study delves into the impact of the interactive agents deployed at the Biosphere Environment Museum in Montreal. This is achieved by examining visitors’ experiences with interactive installations and questioning how these experiences reflect into the daily life. The study adopts a design ethnography research method, employing primary ethnographic and qualitative approaches to collect data. As a result, interactive installations are preferable comparing to non-interactive installations. The study concludes by reflecting on potential future outcomes. Keywords: AI, eco-didactic, sustainability, museological experience, interactivity, interactive learning DOI: 10.7176/RHSS/14-5-06 Publication date: June 30 th 2024
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".