Perceiving Generative AI in Teacher Practice: A Design-Based Case Study in a Graduate Course
Bibliographic record
Abstract
This study presents a design-based case study examining how in-service teachers enrolled in a graduate educational technology course perceive and engage with Generative AI (GAI) tools, such as ChatGPT. As part of a structured course activity, participants incorporated GAI into lesson planning tasks and reflected on its pedagogical usefulness. Two participants completed a post-course survey consisting of Likert-scale and open-ended questions. Results indicate that both educators recognized benefits such as enhanced creativity and planning efficiency, while also expressing concerns about information reliability, ethical boundaries, and over-reliance. The findings highlight variability in teacher engagement based on prior AI experience and emphasize the need for scaffolding, critical reflection, and ethical guidance in AI integration. The study underscores the importance of providing early orientation, scaffolded design tasks, and space for structured dialogue around ethics and pedagogical judgment when integrating GAI in teacher education. Situated within the broader discourse on AI in education, this case study contributes to the literature by offering a context-specific account of how educators explore and make sense of GAI during authentic planning tasks. Design recommendations are proposed for future course iterations and for supporting professional learning environments more broadly.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".