Envisioning AI in Creative Classrooms: Perspectives from Preservice English Teachers in China
Bibliographic record
Abstract
As artificial intelligence (AI) technologies gain prominence in educational contexts, it is important to understand how future educators perceive AI’s role in promoting creativity. This study examines how Chinese preservice English teachers envision the use of AI to support creativity in classroom instruction. Participants included 53 sophomore students from a normal university in China, all of whom are preparing to become K–12 English teachers. Data were collected through a questionnaire consisting of four structured multiple-choice questions and one open-ended question. The survey explored participants’ beliefs about AI’s potential to foster student creativity, their own use of AI tools for creative purposes, their attitudes toward student use of AI for creative work, and perceived challenges in integrating AI into creative education. The final open-ended question invited participants to describe how AI could best support creativity in education. Findings suggest that while many respondents are cautiously optimistic about AI’s creative potential, they also express concerns about overreliance, lack of training, and ethical considerations. Their open-ended responses highlight a desire for AI to serve as a supportive tool—enhancing imagination, offering inspiration, and facilitating personalized learning. This study contributes to the growing body of research on AI in education and provides insights for teacher education programs aiming to prepare educators for creative and responsible AI integration.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".