Towards AI Literacy: 101+ Creative and Critical Practices, Perspectives and Purposes
Bibliographic record
Abstract
“This collection of AI stories and examples in education exemplifies citizen science at its finest. It captures the authentic voices of individuals who are actively testing and expanding their AI literacy, sharing their experiences to support and inspire others. Through their contributions, they collectively advance our understanding and application of AI in educational settings, showcasing the true spirit of community-driven learning and innovation. Your feedback is encouraged; this story is only beginning". \nDr Margaret Korosec, Dean of Online and Digital Education, University of Leeds \n \nThis is the second open crowdsourced collection by #creativeHE which presents creative and critical practices, perspective and purposes from educators, researchers and students between September 2023 and January 2024. \n \nWe are grateful for all 119 contributions from 22 countries including Argentina, Australia, Bangladesh, Brazil, Canada, China, Egypt, France, Germany, Greece, Ireland, Israel, Italy, Mexico, Netherlands, Slovenia, South Africa, Spain, United Arab Emirates, United Kingdom, Uruguay. \n \nAs the collection is made available under the Creative Commons License CC-BY-NC-SA licence, anybody can use the collection as open data to further interrogate the use of AI in Education. Please share any resulting outcomes with the editorial team and the wider community.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".