Re-thinking SoTL for the Age of GenAI
Bibliographic record
Abstract
The rapid advancement of Generative AI (GenAI) necessitates a re-evaluation of established Scholarship of Teaching and Learning (SoTL) frameworks. This paper presents a novel approach to pedagogical research through the lens of diffraction, enabling educators to embrace uncertainty, build trust with technology, and reconceptualise practices in a GenAI-led education landscape. By offering five key propositions, this paper advocates for a multidisciplinary and context-conscious methodology that moves beyond traditional SoTL perspectives and reflective practice. The integration of GenAI into teaching and learning processes is explored as both a challenge and an opportunity, prompting a shift towards teaching innovation. We stress the importance of reimagining SoTL as a dynamic and inclusive field capable of addressing the complexities of a GenAI-driven world. Through our diffractive propositions, educators are encouraged to engage in transformative pedagogical practices that foster human and non-human entanglement, ultimately enhancing and advancing the learning experience in higher education.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.059 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| 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".