Critical GenAI Literacy: Postdigital Configurations
Bibliographic record
Abstract
Abstract Critical Generative Artificial Intelligence (GenAI) literacy cannot be reduced to a universal framework. Rather, it must be understood as a constellation of situated literacies, shaped by disciplinary perspectives, socio-political contexts, and technological affordances. This multi-authored article explores the emerging concept of critical GenAI literacy and its postdigital configurations. Fourteen authors contributed with different sections, followed by five author-reviewers who examined the article as a whole. The objective was to invite diverse perspectives, constructive critique, and promote collaborative efforts to develop a clearer and more inclusive understanding of critical GenAI literacy. The introduction focuses on establishing some initial assumptions about what we mean by ‘critical’ in general, and for GenAI literacy in particular, and why a conjunction of postdigital configurations of the construct emerges as an adequate methodological solution. In the main part of the article, some authors focus on the concept of ‘literacies’ as a starting point for their reflection, while others directly examine critical GenAI literacy through a postdigital perspective. We conclude that critical GenAI literacy requires moving beyond technical skills to engage with AI’s epistemological, ethical, and relational dimensions, ensuring learners critically interrogate its role in knowledge production. Future inquiry should focus on integrating this literacy into education in ways that promote social justice, epistemic diversity, and democratic participation.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".