Digital Interactivity in SpaceAI-Augmented Eco-Didactic Experience in Public Realm
Bibliographic record
Abstract
The ecological crisis is advancing rapidly, and it is crucial to spread awareness, create dialogues, and educate about environment and sustainability to encourage behavioral change and eco-action. The public realm, characterized by its high human circulation, serves as an ideal space to initiate, and foster these environmental conversations. Public artworks stand out as one of the most prevalent and impactful approaches for engaging with the society. In the environmental context, eco-art facilitates sharing of eco-messages, fosters community dialogues around critical issues and solutions, and motivates individuals to take meaningful action. Although public eco-art installations significantly engage audiences, the potential to amplify this impact through Artificial Intelligence (AI) augmented interactive gamification within a didactic framework remains largely unexplored. AI technologies are rapidly infiltrating both professional and personal spheres, significantly influencing consumer behavior through the advertising industry shaping visions for future urban landscapes, as evidenced in conceptual designs for smart cities. However, the high energy consumption associated with AI raises environmental concerns, even as its adoption in daily life becomes inevitable. This research explores how AI technologies can enhance environmental learning by developing an AI-augmented, eco-didactic interactive game. The goal is to support the dissemination of the United Nations’ (UN) Sustainable Development Goals (SDGs) related to the built environment. By integrating AI, interactivity, and gamification with an artistic approach, the study seeks to transform urban spaces into creative and interactive hubs. These engaging experiences aim to spark curiosity, inspire enthusiasm, and encourage proactive eco-friendly actions within the public realm. This research advances studies in creative AI, autonomous AI, and social AI focusing on their integration within physical environments, eco-didactic spaces and public domains in design, fine arts, architecture, urban studies, and environmental fields. It contributes directly to development of engaging environmental and educational public space experiences both in Canada and globally. The findings promise to be broadly applicable, offering a pioneering framework for interactive eco-didactic design practices and providing unique insights for future advancements. By illustrating the benefits of AI in fostering ecological awareness and sustainable engagement, this research supports the dissemination of SDGs, particularly in educating for sustainability within urban environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".