Critical Artificial Intelligence literacy: A scoping review and framework synthesis
Bibliographic record
Abstract
The proliferation of Artificial Intelligence (AI) in everyday life raises concerns for children, other marginalized groups, and the general public. As new AI implementations continue to emerge, it is crucial to enable children to engage critically with AI. Critical literacy objectives and practices can encourage children to question, critique, and transform the social, political, cultural, and ethical implications of AI. As an initial step towards critical AI education, we conducted a 10-year scoping review to identify publications reporting on activities that engage children, between the ages of 5 and 18, to address the critical implications of AI. Our review identifies a wide range of participants, content, and pedagogical approaches. Through framework synthesis guided by an established critical literacy model, we examine the critical literacy learning objectives embedded in the reported activities and propose a critical AI literacy framework. This paper outlines future opportunities for critical AI literacies in the field of child-computer interaction including inspiring new learning activities, encouraging inclusive perspectives, and supporting pragmatic curriculum integration. • In the past 10 years, 30 papers include children in AI-focused critical learning activities. • Synthesis of existing literature to operationalize a critical AI literacy framework. • Future opportunities for critical AI pedagogy in research and educational practices.
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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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.037 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".