Building AI Literacy in Pre-Service Teacher Education in Canada: A Case Study of Two Cohorts
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
Preparing new teachers for the reality of artificial intelligence in education (AIEd) has become a pressing issue. This study was conducted in a Canadian teacher education program that offers a course on digital technologies incorporating a module on AIEd. This paper addresses two research questions: 1) What were teacher candidates’ (TCs’) experiences with the module on AIEd? and 2) What were TCs’ views on the use of AI by themselves and their students? The study employed an explanatory mixed methods design, combining quantitative and qualitative data gathered via a survey administered to TCs directly following their module completion. Participants were two cohorts of TCs (108 TCs in 2024 and 104 TCs in 2025). Findings show TCs’ satisfaction with the module as they highlighted three major benefits: offering useful teaching resources; more acceptance to explore the technology and embrace it critically; and promoting AI literacy. TCs expressed an inclination to use AI as teachers. However, they expressed negative views toward their students’ use of AI. Additionally, most TCs demonstrated developing levels of critical AI literacy, especially among the most recent cohort. This research offers insights into promoting TCs’ AI literacy and presents implications for teacher education research, practice, and policy.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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