Teaching and AI in the postdigital age: Learning from teachers’ perspectives
Bibliographic record
Abstract
This interview-based study aimed to understand how teachers make sense of their work and themselves in relation to artificial intelligence (AI) and other digital technologies, and was conceived as a means of learning with and from teachers. Navigating recent AI developments raised questions about thinking, creativity, production, and the meaning and value of humanity, along with more practical concerns regarding instruction and assessment. Creating policy and ongoing teacher education opportunities that recognize teachers’ capacities for professional judgement while also providing support would encourage thoughtful and creative uses of AI, and avoid pressuring teachers to thoughtlessly rush forward with AI implementation. • Interview-based study of teachers' perceptions and experiences about AI and other digital technologies in education. • Teachers recognized benefits and drawbacks to AI and technology in relation to teaching and learning. • Recent AI developments raised questions about human-human and human-technology relationships. • Findings highlight the value of teachers' professional judgement when considering the future of AI and education.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".