Unpacking Predominant Narratives about Generative AI and Education: A Starting Point for Teaching Critical AI Literacy and Imagining Better Futures
Bibliographic record
Abstract
Abstract: In this article, I draw on both personal experience and professional literature to explore common narratives and assumptions about generative AI (GenAI) and the roles they play in discussions about GenAI’s place in education, libraries, and information literacy. In particular, I explore how misleading narratives of GenAI’s cognitive capacities and inevitability frequently minimize its present and potential harms and encourage people to rapidly and uncritically integrate GenAI technologies into their everyday lives in order to remain relevant in the workplace and in society. Recognizing the pervasiveness of these narratives and reflecting on my own process of making sense of them while also engaging with other framings of GenAI, I advocate for librarians and fellow educators to grow a collective practice of critical inquiry into GenAI that can help inform our teaching practices and our engagement in what is sometimes called critical AI literacy .
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".