Learning with ChatGPT: An Adult Educator’s Journey of Building Critical AI Literacy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it