‘Hands Off’ Learning: The Artifice of Educating in an Algorithmic Age
Bibliographic record
Abstract
Despite dire warnings over AI harms, higher education has chosen complicity over resistance to the algorithmic turn. In this position paper, we argue that this reflects an increasing dependence on automated, screen-biased, information-centric schemes of curriculum, instruction, and assessment that prioritise educational products over learning in practice. In contrast with practice-based hands-on learning, today’s definitively hands-off schemes of educating have all but erased physically embodied, sensory, and authentically situated modalities of learning that are inconvenient for fast credentialing. Far from ‘innovating’ education, as AI proponents claim, the algorithmic turn has made public education more vulnerable to corporate capture. Drawing on transdisciplinary perspectives on technology, teaching, learning, and assessment, this paper extends our earlier explorations of critical algorithmic literacies to consider the ‘artifice’ of educating in an algorithmic age. Accordingly, this is not a paper about AI, or a comprehensive review of the current literature, but a critical analysis of the structural, ideological, and pedagogical compatibilities that underwrite fast credentialing. We conclude with a call for a greater emphasis on hands-on and practice-based approaches to learning, instruction, and assessment that talk back to the pseudo-pedagogical conceits of algorithmic and institutional artifice alike.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.058 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".