AI for Teachers: An Open Textbook—Artificial Intelligence for and by Teachers (2nd edition), by Colin de la Higuera and Jotsna Iyer (Erasmus+, 2024)
Bibliographic record
Abstract
AI for Teachers: An Open Textbook adopts an accessible approach, providing educators with foundational knowledge about artificial intelligence (AI) and its applications in education without delving into overly technical details, making it suitable for teachers seeking to integrate AI into their classrooms.The textbook positions itself at the crossroads of theoretical understanding and practical implementation, addressing AI's benefits and challenges in education.The authors, Colin de la Higuera and Jotsna Iyer, bring significant expertise to the topic.At the time of publishing, de la Higuera had been serving as Chief Equality Advocate at UNESCO's International Research Center on Artificial Intelligence (IRCAI) since 2020, while also holding the Academic Chair on Open Education and AI at the University of Nantes (https://ircai.org/project/ai-and-education).Likewise, Iyer had been actively involved with the Erasmus+ Artificial
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.023 |
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".