Bibliographic record
Abstract
The introduction of widely available generative AI tools has caused a frenzy of both positive and negative reactions. Between utopian visions and apocalyptic predictions of AI’s impact on education, there is a need to thoughtfully consider what education in the age of AI can and should look like. This volume focuses on the implications of AI technology for teachers in K–12 and university settings, providing a careful look at its affordances and drawbacks for social studies curriculum and teaching. Scholars specializing in the field of social studies education provide information and practical ideas for teaching with current technology, alongside frameworks for thinking about future iterations of AI. This book fills a critical need, especially among educators, to consider the current and potential future impacts of AI while avoiding the traps of alarmism or techno-utopianism. Whether skeptical or enthusiastic about AI, every social studies educator will find something useful to their practice in this book. Book Features: First-ever compilation of AI considerations and strategies in the context of social studies education Nontechnical explanations of what AI can do (and not do) in practical educational contexts to enable educators to approach its use with careful judgment Advice for educators to help them assess future iterations of AI technology Critical considerations of AI across multiple contexts (e.g., ethics, equity, multilingual learners, cybersecurity) Work from leaders in technology and social studies education across Canada and the United States Contributors: Erin C. Adams, Curby Alexander, Elizabeth Barrow, Daphanie Bibbs, Ariel Cornett, Matthew Cress, Kevin Donley, Leslie Smith Duss, Lindsay Gibson, Thomas C. Hammond, Marie Heath, Dawnavyn James, Patrick Kane, Dan Krutka, Liran Ma, Tim Monreal, Rachel Moylan, Julie Oltman, Zilong Pan, Michelle Reidel, Elizabeth C. Reynolds, Tina C. Soliday, Vi Trinh, Bretton A. Varga
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".