Toward Agency‐Centered <scp>AI</scp> Literacy: A Scoping Review
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 Digital literacy is well‐studied across disciplines, with established attention to core competencies and social inequalities. However, artificial intelligence (AI) literacy remains underexplored. To address this gap, we conducted a scoping review on AI literacy to: (1) consolidate current definitions and pinpoint conceptual gaps, (2) evaluate methodological approaches and their relevance in practice, and (3) examine how social inequalities are considered in AI literacy studies. Definitions of AI literacy are inconsistent across and within disciplines, and most studies do not consider social factors. Most definitions focus on knowledge and skill acquisition, framing AI literacy as a suite of acquired competencies. We argue that current understandings of AI literacy need to expand to include informed decision‐making, critical engagement, and resistance to technological coercion by taking an agency‐driven approach. These insights can guide researchers, educators, and policymakers in fostering an agency‐centered AI literacy that empowers individuals in an increasingly AI‐mediated world.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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