Generative AI and Teachers’ Perspectives on Its Implementation in Education
Bibliographic record
Abstract
While artificial intelligence (AI) has been integral in daily life for decades, the release of open generative AI (GAI) such as ChatGPT has considerably accelerated scholars’ interest in the impact of GAI in education. Both promises and fears of GAI have been becoming apparent. This quantitative study explored teachers' perspectives on GAI and its potential implementation in education. A diverse group of teachers (N = 147) completed a validated survey sharing their views on GAI technology in terms of its use, integration, potential, and concerns. Overall, the teachers express positive perspectives towards GAI regardless of their teaching style. The findings of the study suggest that the more frequently teachers used GAI, the more positive their perspectives became. The teachers believed that GAI could enhance their professional development and could be a valuable tool for students. Although no guarantee exists that teachers’ perspectives translate into actions, previous research shows that technology integration and diffusion is highly dependent on teachers’ initial views (Ismail et al., 2010; Sugar et al., 2004). The findings of this study have implications on how GAI may be integrated in teaching and learning practices.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".