Preparing Educators to Teach and Create With Generative Artificial Intelligence
Bibliographic record
Abstract
Teachers skilled in using generative artificial intelligence (GAI) have advantages in terms of increased productivity and augmented instructional capabilities. Alongside the rapid advancement of GAI, teachers require authentic learning opportunities to build the confidence and expertise necessary for engaging with these technologies creatively and responsibly. This article provides an illustrative case of preparing preservice and in-service teachers with the knowledge, skills, and mindsets to teach and create with GAI. Using a self-study method to investigate professional practices, we analyzed the curriculum, instruction, and assessment in an upper-level undergraduate course in multimedia design and production. Thirty-five teachers engaged in experiential activities focussed on developing artificial intelligence (AI) literacy, alongside a collaborative assignment to co-author an open-access textbook, Teaching and Creating With Generative Artificial Intelligence. To support equitable and inclusive access to the educational benefits offered by AI, the Student Artificial Intelligence Literacy (SAIL) framework was developed. SAIL facilitates student AI literacy through curriculum engagement and three distinct types of interactions: cognitive, socio-emotional, and instructor-guided. Building on lessons learned from the COVID-19 pandemic regarding the issues with technology training for teachers in Canada, five recommendations are offered to facilitate the meaningful integration of AI literacy in teacher education programs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".