Digital Horizons: Faculty and Student Perspectives on ChatGPT and the Future of English Studies
Bibliographic record
Abstract
The growing body of literature on the uses, challenges, potentials, and ethics of generative AI (Artificial Intelligence) is rich and nuanced; however, such research rarely examines faculty and student perspectives comparatively or in the context of discipline-specific issues and concerns. Since both faculty and students are implicated in shaping a future for their discipline of study, and both are deeply affected by disciplinary policies and standards of practice, it is crucial to situate faculty and student perspectives as a part of a shared discourse rather than two related but distinct conversations. This thesis investigates the specific expectations, concerns, ambitions, and desires for the future that circulates among and between English faculty and students in the wake of the widespread availability of generative AI applications like ChatGPT. It employs mixed-methods to compare and contrast the responses of eleven faculty and thirty-one students from one Ontario university’s English department to semi-structured questionnaires on the topic of generative AI, the future of English studies, and participants’ perceptions of one another. Participant perspectives are contextualized within a discussion of the imagination as a mechanism for inventing into being. This research emphasizes self-reflexivity as a method for establishing trustworthiness. This MA thesis finds that participants imagine generative AI and one another in both similar and contrasting (and occasionally contradictory) ways. In that context, the thesis ends by discussing misconceptions and mistrust among and between faculty and students as a potential cause for differences between what participants anticipate and what they desire for the future of their field.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".