From AI Literacy to AI Competency: Fostering Human Agency and Critical Thinking through Andragogical Practice
Bibliographic record
Abstract
The growing integration of Artificial Intelligence (AI) into education requires deliberate attention to enhance both AI Literacy and AI Competency. However, most current AI education frameworks focus on literacy, emphasizing development of static knowledge and skills through rigid pedagogical approaches. This paper proposes a conceptual framework positioning AI literacy and competency as gradual stages in the development of AI Fluency. Our framework outlines pedagogical and andragogical teaching-learning approaches based on three key practice dimensions: (i) teaching and learning ethos, (ii) teaching plan, and (iii) learning dynamics. While pedagogical methods are best suited to AI literacy, andragogical strategies emphasizing human agency, critical thinking, and contextual application are more appropriate for fostering AI competency. This framework contributes to AI education research by offering a structured model for curriculum development, faculty training, and instructional design, ensuring that AI education moves toward fostering lifelong AI skills.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".