Education and Artificial Intelligence at the Scene of Writing: A Derridean Consideration
Bibliographic record
Abstract
Skepticism of the written word has been prevalent in philosophical discourse at least since the works of Plato. This article employs philosophical method. It situates the ongoing educational concern with AI Chatbots in terms of this skepticism toward writing. Specifically, this longstanding skepticism posits that the written word is an alienated form of the spoken word. This article demonstrates how two prevalent traditions of education—traditional and progressive—take up this same skepticism. The article calls upon the work of Jacques Derrida, whose deconstructive theories on Plato and the written word problematize this line of writerly skepticism. Derrida’s work on Rousseau’s Emile informs a more general approach to pedagogy which entails what Derrida calls “the logic of supplementarity .” This “logic” involves the paradoxical debt that writing owes to speech. Thus, one can discern a distinct sense in which education—in general—is implicated in a tension that exists between the written word to the spoken. Ultimately, this articles suggests that the ongoing concern with AI Chatbots—linked to an ancient skepticism toward writing—is none other than a concern with the very practice of education per se.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".