Teaching and AI in the postdigital age: Learning from teachers’ perspectives
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
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.
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 | 0.002 | 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 | 0.002 | 0.001 |
| 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