Teachers Perceptions & Attitudes towards Artificial Intelligence (AI) Integration in Suburban School
Bibliographic record
Abstract
In the digital transformation era, educators are exploring the potential of artificial intelligence (AI) to enhance learning. This study investigates teachers' perceptions and attitudes regarding artificial intelligence (AI) integration in suburban schools. A quantitative survey methodology was employed, using a structured questionnaire distributed via Google Forms to suburban school teachers. The questionnaire consisted of four sections: demographic information, perceived usefulness, perceived ease of use, and attitudes toward AI. Responses were measured using a five-point Likert scale to assess teachers' agreement levels with various AI-related statements. A total of 250 questionnaires were distributed between February 1 and May 30, 2024, yielding 205 valid responses (82% response rate). The study found that in terms of perception, suburban teachers generally view artificial intelligence (AI) as a valuable tool in their work. They believe artificial intelligence (AI) can help them personalize learning for students, address individual needs, improve their teaching skills, and reduce the time spent on administrative tasks. In the context of attitudes, teachers are particularly excited about how artificial intelligence (AI) can enhance creativity and problem-solving in the classroom and improve their overall teaching effectiveness. Their attitudes suggest enthusiasm for AI's potential to foster creativity and problem-solving in classrooms. These positive perceptions and attitudes highlight a readiness among teachers to integrate AI into their teaching practices.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".