Navigating Generative AI Tools in the Classroom Through a Lens of Equity and Accessibility
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.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".