Conversations with students on self-regulated learning with GAI
Bibliographic record
Abstract
How can learning developers foster AI literacy while helping students use AI ethically in their academic work? Recent research (Lin et al., 2024) shows students value generative artificial intelligence (GAI) for levelling the linguistic playing field, particularly for multilingual writers, while expressing concerns about unethical use, reduced critical engagement, and the flattening of linguistic diversity. As GAI literacy can support academic success (Seo et al., 2021; Hashim et al., 2022; Barrot, 2023), learning developers play a critical role in supporting students' self-regulated AI use to maximise learner benefits while avoiding pitfalls. In this workshop, we shared our framework for teaching AI literacy in academic writing, building on Winne and Hadwin's (1998) COPES model of SRL and recent work on AI literacy (Allen and Kendeou, 2024). Our five-phase model integrated skills in prompt formation, output evaluation, and ethical AI use (see: https://wordpress.kpu.ca/gaiwriting/, Vytasek and Page, 2025). Participants will explore implementing this framework across disciplines through educational scaffolding that addresses (a) defining the writing task; (b) planning and goal setting; (c) using learning strategies; and (d) applying metacognitive processes to evaluate, reflect, and improve on both task completion and broader skill development.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".