Education à l’intelligence artificielle : Quelles compétences acquérir par les élèves ?
Bibliographic record
Abstract
Artificial intelligence is a concept that generates different types of reactions from educational actors. First of all, it is a concept that may seem very far from educational reality and generate fears related to the type of data processing that will be used. On the other hand, too positive expectations could also be expected of AI in education. To help demystify this concept, let's start by defining it. Among pioneers in the field like Minsky, artificial intelligence is defined as "the construction of computer programmes that engage in tasks that are, for the time being, performed more satisfactorily by human beings because they require high-level mental processes such as perceptual learning, memory organisation, and critical reasoning" (1956). More recently, Young et al. (2019) defined artificial intelligence "as any domain-specific system that uses machine learning techniques to make rational decisions about non-deterministic tasks". Taking into account the multiplicity of disciplinary fields and the number of non-deterministic tasks that learners face, we can already realise that the uses of AI in education will be very specialised and will not be able to cover all the broad fields of competence of teachers.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".