101 creative ideas to use AI in education, A crowdsourced collection
Bibliographic record
Abstract
This open crowdsourced collection by #creativeHE presents a rich tapestry of our collective thinking in the first months of 2023 stitching together potential alternative uses and applications of Artificial Intelligence (AI) that could make a difference and create new learning, development, teaching and assessment opportunities. Experimentation is at the heart of learning, teaching and scholarship. Being open to diverse ideas will help us make novel connections that can lead to new discoveries and insights to make a positive contribution to our world. Ideas shared may be in its embryonic stage, but worth exploring further through active and creative inquiry. We would like to illuminate the importance of responsible, critical and ethical use of AI in education settings and more generally. We are grateful for all 101 contributions from 19 countries: Australia, Canada, China, Egypt, Germany, Greece, India, Israel, Italy, Ireland, Jordan, Liberia, Mexico, South Africa, Spain, Thailand, Turkey, United Kingdom and the US. A special thank you to Bushra Hashim for the beautiful design. Suggested citation: Nerantzi, C., Abegglen, S., Karatsiori, M. and Martinez-Arboleda, A. (Eds.) (2023). 101 Creative ideas to use AI in education. A collection curated by #creativeHE. Graphic Design by Bushra Hashim. CC-BY-NC-SA 4.0. As the collection is made available under the Creative Commons License CC-BY-NC-SA license, 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 editors “This collection represents vision; it embodies creativity. The importance of perspective and community of practice comes to life here in the breadth of examples demonstrating creative ideas to use AI in education. As we explore how we design new experiences for our learners and differentiate opportunities to engage in new ways, we have an opportunity to push our own boundaries and explore. We can collaborate, radically. This is a collection that will only grow as we shift our own practice and as we allow ourselves to experiment and iterate for a transformational student experience.” Dr Margaret Korosec, Dean of Online and Digital Education, University of Leeds.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.037 |
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".