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.
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.
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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".