“I’m Not Worried about Robots Taking Over the World. I Guess I’m Worried about People”: Emoting, Teaching, and Learning with Generative AI
Bibliographic record
Abstract
Qualitative studies that examine the impact of generative AI technologies on higher education remain scant. Whether it is the ethical dimensions of modeling human emotions within these technologies or the authentic emotional reactions to these technologies and their outputs—emotionality is at the centre of generative AI discourse. This paper reports findings from a study exploring educators’ emotional responses to the integration of generative AI and higher education. We conducted semi-structured interviews with 37 multidisciplinary faculty at the University of Toronto Mississauga (26% response rate). We first describe the data collection process, including an overview of the institutional context. We then outline a historical context to frame our examination of educators’ self-reported emotional responses to teaching, learning, and living with generative AI. Most respondents expressed ambivalence of some variety, and we noted disciplinary patterns regarding the type of fears and excitements respondents reported. The paper concludes with two pedagogical provocations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".