Insidious chatter versus critical thinking: Resisting the Eurocentric siren song of AI in the classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article contributes to the ongoing discussion about the impacts of utilizing emerging technologies – especially AI learning – in higher education. After reviewing the pros and cons of using ChatGPT in the classroom as they are typically considered, I raise a deeper, less frequently addressed concern: the pervasive persistence of Eurocentric biases in the academy and the danger that AI-empowered software will reinforce them further. To this end, I present the findings of a simple experiment I conducted, directing ChatGPT to produce and refine a syllabus for an undergraduate course on modern political philosophy, together with an essay responding to one of the questions set in the syllabus. The results clearly demonstrate the grave potential for such supposedly ‘time-saving’ technological shortcuts to re-inscribe Eurocentric thinking and unconscious biases, thus seriously complicating the vital, already challenging task of decolonizing the academy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it