Preserving Teacher and Student Agency: Insights from a Literature Review
Bibliographic record
Abstract
Abstract This chapter is a comprehensive literature review that explores the influence of artificial intelligence (AI) on education and its implications for human agency. It begins by highlighting the potential advantages of incorporating AI in education, such as intelligent tutoring, learning assessment, and dropout prevention. The chapter then raises important questions regarding the roles of teachers and students in an AI-driven education system, contemplating whether AI can replace or enhance the capabilities of human educators. These inquiries initiate a discussion on the ethical considerations surrounding the deployment of AI in education. To provide necessary context, the chapter offers definitions of AI in education and introduces two theoretical frameworks: the technician system theory and the concept of agency. The technician system theory suggests that as AI systems become more sophisticated, there is a risk of neglecting the complexity of teaching techniques and the expertise of human educators. The concept of agency is then presented as a means to comprehend and position human activity in relation to AI systems. The chapter explores agency from both the computer science and social science perspectives, emphasising the autonomy and decision-making abilities of both AI systems and individuals. With the focus on identifying AI applications that may limit teacher and student agency, the findings reveal that the integration of AI systems in education can lead to power imbalances and reduce the decision-making authority of teachers, shifting expertise to programmers and system designers. Likewise, student agency can be compromised by AI systems, particularly in task assignment and gamification. Ultimately, this chapter underscores the significance of preserving human agency in the context of AI in education. It advocates for the design and implementation of AI-based tools that enhance agency rather than restrict it. The findings of the literature review shed light on the ethical implications and challenges associated with maintaining agency in the era of AI in education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".