Supplementing AI “Curriculum” Using Teachers Pay Teachers Resources: What There Is and What There Isn’t
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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