Créativité assistée ou créativité entravée? L’enjeu de l’intégration des IA génératives dans les formations académiques à la communication visuelle
Bibliographic record
Abstract
L’article explore les enjeux de formation des futurs professionnels de la communication visuelle aux intelligences artificielles génératives (IAG) dans les cursus académiques de formation au Canada, en France et en Tunisie, en insistant sur la nécessité d’une réintermédiation des savoirs. À partir d’une enquête qualitative menée auprès d’enseignants et de professionnels, il met en lumière le basculement des compétences manuelles vers des compétences linguistiques, notamment autour du « prompting ». Cette mutation appelle une pédagogie, qui articule expérimentation, culture esthétique, esprit critique et littératie des données. Former à la créativité assistée par l’IA ne saurait donc se limiter à l’usage technique des outils, mais suppose de rendre visibles les médiations algorithmiques et d’accompagner les étudiants dans une appropriation consciente et critique de ces dispositifs.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".