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". Dr Margaret Korosec, Dean of Online and Digital Education, University of Leeds This 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. We 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. As 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. The collection has been generously supported by the Imagination Lab Foundation through the Playful Hybrid Higher Education project (https://playhybrid.education/) led by Sandra Abegglen and situated in the School of Architecture, Planning and Landscape at the University of Calgary. Thanks go also to #creativeHE of which we, the editors, are all part of and that has acted as supporter of the creative AI collections from the very beginning. The #creativeHE community hosts all calls and dissemination activities for the AI collections on their website: https://creativehecommunity.wordpress.com/ - A special thank you to Leonor Agüero Vivas for the beautiful design.-
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".