Teachers’ Awareness in Artificial Intelligence and Digital Competence in the Workplace
Bibliographic record
Abstract
This study explores how artificial intelligence (AI) influences education, particularly in teaching, learning, and assessment. While AI offers benefits such as personalized learning, automation, and data-driven feedback, it also brings challenges such as data privacy, teacher adaptation, and equity concerns. This research examines the link between teachers’ awareness of AI and their digital competence in the workplace. Using a descriptive correlational design, data were collected via an online survey from 144 public basic education teachers in Laguna, Philippines. The study employed validated tools: the AI Awareness Scale and the Digital Competence Questionnaire. Results showed that only one factor—attendance at AI or ICT-related training—significantly influenced AI awareness (p = .044). Thus, the hypothesis was partially accepted, as the other demographic attributes showed no significant differences. However, a notable finding is the rejection of the hypothesis that no significant relationship exists between AI awareness and digital competence, suggesting a meaningful connection between the two. This research provides new insights into a relatively unexplored area: how teachers’ understanding of AI correlates with their ability to effectively use digital tools. Although AI’s role in fields such as health care and technology is well studied, its educational impact, particularly on teachers’ preparedness, remains underrepresented.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".