Detecting Innovators in the Field: Teachers’ Perceptions and Adoption of Generative AI in Education
Bibliographic record
Abstract
The adoption of Generative Artificial Intelligence (GenAI) has gained popularity since late 2022, sparking discussions about its role in education. An important issue is understanding teachers' perceptions of this technology, given that teachers are seen as key actors in integrating GenAI into teaching and learning processes. This qualitative research explores secondary school teachers' perceptions of GenAI, using an adapted Technology Acceptance Model (TAM) and Rogers' Diffusion of Innovation Model. TAM, known for assessing user acceptance of technology, was employed to gauge perceptions, while Rogers' model provided insights into how teachers distribute across GenAI adoption stages, from innovators to late adopters. Data was collected through semi-structured interviews and an online survey with 20 in-service teachers from Flanders, Belgium. Findings reveal mixed attitudes among teachers towards GenAI, as participants express enthusiasm about its potential for time-saving and personalized learning benefits, while also voicing significant concerns about plagiarism, GenAI’s trustworthiness, and its possible negative impact on students’ cognitive abilities. The study also highlights the current lack of sufficient training and support for teachers integrating GenAI.
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".