Exploring Learner-Action Timing in a Generative AI Supported EFL Ideathon: A KPT Study in Japan
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 generative AI (GenAI) becomes ubiquitous in education, clarifying how learners and educators perceive, and co-design technology is a pressing challenge. This study involved a one-day participatory ideathon in Japan, with nine pre-service English teachers and six high school students co-creating English lesson ideas that integrate GenAI and textbook-based instruction. Using the Keep-Problem-Try framework, participants submitted one hundred sixty-one reflective sticky notes and fifty-five unique lesson proposals. Qualitative analysis was conducted using open and axial coding to identify thematic categories, while the quantitative analysis applied a rubric-based evaluation by GPT-4o across three dimensions: innovativeness, feasibility, and pedagogical alignment, followed by Mann-Whitney U tests for group comparison. The results showed a strong tendency toward experimental approaches, as indicated by the predominance of “Try” entries and a consistent emphasis on UI/UX usability across all categories. These patterns emphasize the foundational role of interface design and highlight the need to control for design-bias when conducting knowledge-based engineering (KBE)-oriented experiments. No statistically significant differences were found between finalist and non-finalist lesson ideas, indicating a convergence in participants’ design perspectives regardless of finalist status. Additionally, pre- and post-workshop surveys analyzed via Wilcoxon signed-rank tests revealed a significant increase in participants’ expectations for GenAI in education (p <.05), confirming the ideathon’s effectiveness in transforming perceptions. These findings offer design guidelines for future KBE experiments with GenAI, particularly regarding baseline conditions and interface specifications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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