The impact of artificial intelligence on adventure education and outdoor learning: international perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it