Unpacking Predominant Narratives about Generative AI and Education: A Starting Point for Teaching Critical AI Literacy and Imagining Better Futures
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
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 .
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| 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