Teaching high school students about generative AI: Cases of teacher lesson design
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
Teachers who wish to enact lessons about generative AI are required to simultaneously learn about it and develop curricula with activities that align with their discipline. We present two cases of high school teachers, June and Margot, who had different prior experiences, resources, and learning goals related to GenAI instruction. We found that they designed lessons that positioned GenAI as an object-of-study or subject-specific, but neither lesson solely focused on either approach. Prior disciplinary and lesson planning knowledge and in-the moment student reactions to activities shaped their appraisals of lesson effectiveness. However, we observed that co-design experiences and activities were central for helping to develop teachers’ pedagogical design capacity for GenAI. We contribute two cases that illustrate how co-design can support high school teachers who wish to integrate GenAI into their discipline, and by offering contrasting models of pedagogical approaches.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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