Learning with ChatGPT: An Adult Educator’s Journey of Building Critical AI Literacy
Bibliographic record
Abstract
Critical AI literacy is an active area of scientific research and current scholarship on the integration of generative AI technologies in language education. However, there is a dearth of research into Canadian adult educators’ perceptions of and experiences with critical AI literacy development from an autoethnographic perspective. To address this research lacuna, the author conducted a narrative study of his college English for academic purposes classes over three academic semesters in 2024 and 2025. The data, generated from the researcher’s teacher learning journal and regular interactions with ChatGPT as a reflective partner, highlighted three main research results and implications for pedagogical practices. First, developing adult educators AI literacy is a form of teacher professional learning, which can position the learners as class collaborators and knowledge co-creators. Next, adapting teaching approaches to sustain more human-focused learning experiences involves three levels of complexities: between the educator and the chatbot, the learners’ interactions with AI technologies, and the teacher-learner relationship as one of partnership and exploration. Last, to engage the students as active agents in the process of learning, adult educators should craft sound pedagogical approaches to enhance language teaching, stimulate learner participation, and create human-focused teaching interventions in AI-enhanced higher education settings.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".