Supplementing AI “Curriculum” Using Teachers Pay Teachers Resources: What There Is and What There Isn’t
Bibliographic record
Abstract
Artificial intelligence (AI) has had an increasing presence in K-12 education over the last 3 years and is entering the educational practices of both teachers and students. Schools and teachers feel the pressure to both adopt AI-related practices as well as teach their students about AI, but few formal curriculum resources exist for this topic. Teachers typically turn to other resources, such as online educational resource marketplaces (OERMs), to obtain supplemental curriculum materials. The website Teachers Pay Teachers (TPT) started in 2006 to allow teachers to share teaching resources with the goal of helping them teach their students better through learning from each other. Various sources suggest that, in general, many teachers (a) frequently use the TPT platform (up to 85% of U.S. K-12 teachers) and (b) acquire both paid and unpaid resources from TPT to use in their classrooms. In this study, we examine 48 AI-related resources (24 requiring payment and 24 that are free, all provided in a search on the TPT website) that are available to teachers, documenting and analyzing what information teachers are provided to make decisions, comparing free and paid resources, and evaluating the quality of the AI-related resources. We conclude that there is considerable room for improvement in the available resources from both a content and a pedagogical perspective, that professional development on how to effectively evaluate supplemental curriculum resources is needed, and that individuals with stronger backgrounds in computer technologies should be contributing more supplemental curriculum resources to TPT on AI.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".