Educational Approaches for Environmental Sustainability
Bibliographic record
Abstract
The planetary crisis, driven by abrupt climate change, calls for urgent action that integrates theory and practice. Environmental education has a crucial role to play in promoting change, advocated by scholars such as Bourdieu (1986) and Giddens (1990, 2009). This study examines how smart technologies, particularly artificial intelligence and virtual reality, can support teachers in environmental education. It examines Italian initiatives, including the metaverse-based courses at the University of Toronto and the AI-supported teaching activities of Didacta Italia 2023. Smart technologies are not neutral tools, but mediators that shape human-environment interactions, influencing behaviour and consumption patterns, in line with Latour’s (2005) theories. The study evaluates the integration of these tools into environmental education curricula for Italian secondary school students, enhancing learning and promoting sustainability awareness. The study also addresses ethical issues, drawing on discussions by Heidegger (1977) and Trotta et al. (1981, 2023) on AI ethics. By experimenting with these tools in student workshops, the research aligns with Habermas’s concept of “communicative action”, emphasising dialogue for a sustainable society. The results provide insights into balancing innovation and responsibility, contributing to the academic and practical discourse on environmental education and sustainable development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".