Impact van intelligente technologie op onderwijs
Bibliographic record
Abstract
Dit paper bespreekt de impact van intelligente technologie, in het bijzonder ChatGPT in het onderwijs. Wat is de rol van intelligente technologie in het onderwijs? Intelligentie technologie vindt in toenemende mate ingang in het onderwijs en in het dagelijks leven, waar leerlingen er gebruik van maken en docenten zich ertoe zullen moeten verhouden. Maar wat is de mogelijke impact van deze technologie op het onderwijs? En is deze anders dan wat we al kennen? Of het nu gaat om de rekenmachine, het internet of om het digibord, het onderwijs verandert immers steeds mee met of door technologische ontwikkelingen. In dit artikel plaatsen we de opkomst van intelligente technologie in een historisch perspectief en proberen vandaar uit de overeenkomsten, verschillen en impact van intelligente technologie op het onderwijs te duiden.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".