The need for critical digital literacies in generative AI-mediated L2 writing
Bibliographic record
Abstract
This article asserts that the use of generative AI (GenAI) technologies for L2 writing needs to involve critical digital literacies. Drawing on the initial insights from a case study exploring the GenAI practices of secondary school students in Canada, this paper highlights emergent issues surrounding the dispositions of these learners towards these tools, the designs of platforms, and the material differences in the way these tools generate responses and encourage specific practices. Recognizing the inequalities that circumscribe the use of these technologies, this paper proposes materiality , indexicality , and ideology as key constructs that help develop an understanding of critical digital literacies relevant to GenAI-mediated L2 writing and digital multimodal composing. These constructs draw attention to how platform designs and other material processes, together with learner access to resources, can steer learners toward particular interactions and discourses. By understanding how GenAI platforms trained on large datasets can privilege certain ways of thinking and writing, L2 writers can develop a more critical perspective of how these technologies can shape the way we write ourselves into being.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".