The impact of artificial intelligence on adventure education and outdoor learning: international perspectives
Bibliographic record
Abstract
This is a composite article which brings together the international perspectives of the editorial board of the Journal of Adventure Education and Outdoor Learning to explore the impacts of artificial intelligence (AI) on the field of adventure education and outdoor learning (AE/OL). Building on the AE/OL profession’s response to the impacts of COVID-19 on outdoor and environmental education in 2020, this article includes authors from 10 countries including Australia, Brazil, Canada, England, Japan, Kenya, the Netherlands, New Zealand, Norway, and Wales. The statements discuss the impacts and opportunities of AI for the AE/OL professions, researchers, the nature of being in and with the outdoors, and Indigenous knowledges. The intention of this article is not to present a definitive summary of the state of the profession, but to provide examples of the ways in which diverse people are responding to the challenges and opportunities of AI. By sharing these views, and identifying some commonalities, we hope that AE/OL educators, practitioners, researchers and managers can creatively and cautiously seize the opportunities of this technological revolution.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".