Toward Agency‐Centered <scp>AI</scp> Literacy: A Scoping Review
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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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 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".