Navigating Generative AI Tools in the Classroom Through a Lens of Equity and Accessibility
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
The open release of ChatGPT in late 2022 sent the world of education into a frenzy. Popular media outlets both sang the praises of generative AI and also posed questions about what generative AI meant for the future of teaching and learning. In the year that has followed, we have seen a broad range of strategies for managing generative AI on campuses ranging from total bans to open embrace and encouragement. As such, many instructors feel lost and unguided in how to approach generative AI in their classrooms. In response to lack of clarity on direction or alternatives, an increasing number of administrators and instructors are considering bans on generative AI tools. In this article, we offer a set of considerations about the landscape of generative AI in classroom and work settings followed by a set of three models (high, medium, and low use) instructors can use in small, medium, and large classrooms to navigate AI in a higher education setting.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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