Document Analysis and Information Behavior of Pre-Service Teacher Perceptions of ChatGPT Generated Lesson Plans
Bibliographic record
Abstract
This study examined documents produced by generative artificial intelligence as well as users’ perceptions of the usefulness of the documents themselves. The researchers performed document analysis on two generations of lesson plans created with ChatGPT versions 3.5 and 4o. Additionally, three semesters of pre-service childhood educators also reviewed the lesson plans for accuracy and adherence to their course assignment goals. This poster will share findings and trends as well as implications for information practice use of as generative artificial intelligence increases in higher education. Analyse documentaire et comportement informationnel des enseignants·tes en formation sur leur perception de plans de cours générés par ChatGPT RésuméCette étude examine les documents produits par intelligence artificielle générative, ainsi que la perception des utilisateurs·trices sur l’utilité des documents eux-mêmes. Les chercheur·euses ont effectué une analyse documentaire à partir de deux plans de cours générés avec les versions 3.5 et 4o de ChatGPT. En outre, pendant trois semestres, des éducateurs de la petite enfance en formation ont également vérifié l’exactitude et la conformité des plans de cours avec leurs objectifs d’enseignement. Cette affiche présente les résultats et les tendances, ainsi que les implications pour les pratiques informationnelles, à mesure que l’intelligence artificielle générative prend de l’ampleur dans l’enseignement supérieur. Mots-clésintelligence artificielle; analyse de contenus quantitatif; conception de pré-test-post-tes; pratique de l’information
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".