Extending AI-TPACK: Reframing Teacher Readiness for the Generative-AI Era
Bibliographic record
Abstract
Abstract: The increasing presence of artificial intelligence (AI) in education presents both opportunities and challenges for K–12 teacher education. As educators prepare students for an AI-driven world, their readiness to integrate generative AI (GenAI) tools into classrooms becomes crucial. Building on the emerging AI-TPACK framework, this paper extends its theoretical scope to the generative era by introducing three novel dimensions including prompt literacy, ethical AI engagement, and teacher-AI-student partnerships, and examining their implications for teacher readiness and professional learning. Guided by three questions on readiness, competency development, and institutional response, the paper synthesizes current research on GenAI integration and proposes a Strategic Roadmap that operationalizes this extended AI-TPACK model through curriculum design, faculty development, and ethically grounded, equity-focused preparation. Together, the extended AI-TPACK model and the Strategic Roadmap position teacher readiness for GenAI as a paradigm shift in educational thought and practice.
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.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".