AI in classrooms: Impact on teacher identity and autonomy
Bibliographic record
Abstract
The integration of artificial intelligence (AI) in contemporary classrooms is redefining educational processes and teacher roles. Tools such as ChatGPT, automated grading systems, and personalized learning platforms are being widely adopted for their ability to enhance efficiency, deliver adaptive instruction, and support data-driven decision-making. However, this digital transformation also brings forth critical questions about the evolving identity and autonomy of educators. This paper explores how the growing presence of AI in education is impacting teachers' sense of professional identity and instructional autonomy. Drawing on qualitative data collected through interviews and surveys with school teachers across various settings, the study investigates their perceptions of being supported versus replaced by AI tools. Findings reveal a mixed response: while some educators appreciate AI's potential to reduce administrative burdens and enhance personalized learning, many express concerns over diminishing pedagogical control and the fear of obsolescence. Through the lenses of Foucault's theory of surveillance and Freire's critical pedagogy, the paper examines how AI can both constrain and empower teachers depending on implementation and policy context. Teachers with adequate technological support reported a greater sense of agency and viewed AI as a collaborative tool. Conversely, in rigid AI-driven frameworks, teachers felt disempowered and monitored. The paper concludes by emphasizing the need for inclusive AI policy frameworks that involve teachers in decision-making, prioritize professional development, and reaffirm the irreplaceable human dimensions of teaching, empathy, mentorship, and moral judgment. Responsible integration of AI must support rather than substitute the educator's core role in the learning process.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".