Artificial Intelligence in Education: Investigating Teacher Attitudes
Bibliographic record
Abstract
This study aims to investigate teachers' attitudes towards AI in education, focusing on identifying the perceived benefits, challenges, and ethical considerations associated with AI integration into teaching and learning environments. Utilizing a qualitative research design, this study conducted semi-structured interviews with 28 educators from various educational levels and disciplines. Thematic analysis was employed to analyze the interview data, identifying key themes and concepts related to teachers' perspectives on AI in education. Four main themes were identified: Pedagogical Impacts, Ethical and Social Considerations, Technological Challenges and Opportunities, and Perceptions of AI in Education. Pedagogical Impacts encompassed enhancing learning outcomes, curriculum integration, and the evolving roles of teachers. Ethical and Social Considerations highlighted concerns over data privacy, bias, and equity. Technological Challenges and Opportunities discussed integration challenges and the future of educational technology. Lastly, Perceptions of AI in Education revealed varied attitudes, awareness levels, and perceived impacts on professional identity. Teachers recognize the transformative potential of AI in enhancing personalized learning and operational efficiency. However, concerns about ethical issues, technological infrastructure, and the need for professional development are significant. Addressing these concerns requires targeted efforts from policymakers, educational leaders, and technologists to foster a supportive environment for AI integration in 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".