Exploring pre-service teachers’ attitudes and experiences with generative AI: a mixed methods study in Norwegian teacher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As Artificial Intelligence (AI) systems, including generative AI (GenAI), evolve, they offer new opportunities for information access and feedback across disciplines. However, limited research has examined pre-service teachers’ attitudes towards GenAI, particularly their perceptions, experiences, and usage. This study addresses the gap using a mixed methods approach, combining survey data from 209 Norwegian pre-service teachers with interviews from 11 of the same participants. Most were aware of GenAI and had used AI tools, though none considered themselves experts. Statistical analysis identified key predictors of AI knowledge and beliefs about GenAI’s usefulness in teaching. Participants acknowledged GenAI’s practical benefits for lesson planning and content discovery but raised concerns about its trustworthiness and implications for human-centred education. Interviews offered deeper insight into differences between experienced and novice users. The study highlights implications for teacher education, emphasising the need to develop AI competency to prepare future educators for an increasingly digital landscape.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it