Prompting Teacher Identities: A Model for Teacher Subjectivities Constituted Through Artificial Intelligence
Bibliographic record
Abstract
Artificial intelligence (AI) transforms the ethical and moral subjectivity of teachers, positioning them to navigate the complex convergence of technological advancement, intellectual autonomy, and teacher identity. The purpose of this paper is to offer a conceptual model for how teachers’ identities are constituted through AI prompt engineering. Poststructuralist theories are used to examine how the integration of AI in education reshapes the constitution of teachers' identities, drawing from Michel Foucaut’s concepts of discourse theory, power/knowledge, governmentality, subjectivities, and technologies of the self. Focusing on three of Foucault’s specific technologies of the self—the confessional, the panopticon, and the examination—the paper examines how AI prompt engineering can be considered as a site of governmentality. A conceptual model, “teacher subjectivities constituted through artificial intelligence” (TSCAI) is suggested to illustrate the relationships among the theoretical concepts in a visual format. Reflective questions are posed for teachers to investigate how the model applies to their AI prompt engineering. Implications for practice and research of the TSCAI model are discussed, followed by a recognition of the limitations of the model. The paper concludes with suggestions for using the model in teacher-education contexts and encourages teachers to acknowledge their own identities while working with AI.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".