Didactic experiences in the public realm: AI, interactivity, and playfulness for empowering eco-change
Bibliographic record
Abstract
Artificial Intelligence (AI) rapidly adapts to diverse audience engagement modes in Digital Arts, either interactive or non-interactive forms. These engagements create potential alliances for intelligent eco-didactic environments in the public realm. Studies show that interactivity positively affects the learning experience; hence, the coalition between AI and Digital Art has the potential to result in enhanced eco-art experiences. This collaboration could augment the eco-message and lead to behavior shift. This paper explores the interactive engagement modes in different art mediums to identify their eco-didactic potential. The study adopts a mixed methods approach, engaging with causal-comparative qualitative content analysis research. We collected secondary data from various mediums to define the characteristics of the engagement modes in eco-art, digital art, and AI artworks. Finally, we interviewed mixed-media artists to explore the technologies used in these art mediums, the different engagement modes they adopt, and eco-didactic possibilities. As a result, we found that incorporating aspects such as interactivity, coherence, aesthetics, playfulness, and meaning, can increase the impact of eco-didactic experiences. In addition, AI creates new possibilities for these experiences with its popularity and features such as real-time data utilization, personalization, and generative reciprocal dialogues which facilitate the understanding of complex environmental issues.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".