From Understanding to Creating: Bridging AI Literacy and AI Fluency in K-12 Education
Bibliographic record
Abstract
This paper explores the distinctions and connections between AI literacy and AI fluency, drawing parallels with the historical development of other literacies such as computer literacy and digital fluency. The paper argues that while AI literacy focuses on understanding and evaluating AI technologies, AI fluency represents a higher-order competency encompassing innovation, ethical management, and creation with AI. Examining existing definitions identifies "creation" as a recurring theme differentiating fluency from literacy, where fluency implies the ability to generate novel solutions and artifacts using technology. The paper proposes a conceptual framework for AI literacy and fluency in K-12 education, emphasizing the need to develop both concurrently rather than sequentially. By fostering AI literacy through comprehensive professional development, educators can equip themselves and their students to engage with AI ethically and effectively. Simultaneously, cultivating AI fluency empowers students to utilize AI as a tool for innovation and problem-solving, going beyond passive understanding to actively shape the future of AI in education. The paper concludes that investing in teacher training and developing clear definitions of AI literacy and fluency are crucial steps toward integrating AI into K-12 education responsibly and effectively, preparing students to navigate the complexities of an increasingly AI-driven 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".