Critical digital literacies, generative AI, and the negotiation of agency in human-AI interactions
Bibliographic record
Abstract
Drawing on the notion of critical digital literacies (CDL) ( Darvin, 2017 ; 2025a ), this qualitative case study investigates the extent to which multilingual secondary school students in Canada exercise agency when they interact with generative artificial intelligence (GenAI) technologies. Recognizing how learners have unequal access to resources that involve the use of GenAI, it poses the question: To what extent do learners enact CDL, and what enables or constrains their enactment? Data were collected through interviews, observations of learners' GenAI usage, and digital artifacts, including screenshots of prompts and AI-generated texts. Thematic analysis was employed to understand learners’ dispositions towards these technologies, and their GenAI practices, while multimodal analysis provided insights into how learners negotiated their agency as they interacted with GenAI platforms. Represented by two exemplar cases, findings show how learners who used GenAI primarily through apps in mobile phones or chatbots embedded in social media platforms, tended to adopt a passive stance towards GenAI, accepting generated output without much critical evaluation. In contrast, learners with access to a laptop and multiple GenAI platforms, demonstrated a more critical disposition towards this technology, comparing outputs across platforms, questioning their reliability, and verifying information through lateral reading. By drawing attention to how material conditions of access and platform design can constitute dispositions towards GenAI and CDL, this study calls on educators to support learners in identifying and challenging inequalities and ideological biases embedded in these technologies, to foster more equitable and inclusive GenAI literacy 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".