Ethical and effortful: workshopping human and generative AI academic writing collaborations
Bibliographic record
Abstract
The launch of Open AI’s ChatGPT in 2022 caused a furore within higher education. While initial reactions were negative – educators imagined the end of the undergraduate essay and an acceleration in academic integrity departures – more recent conversations have emphasised how these tools might enhance teaching and learning experiences. This paper explores one possibility for approaching student use of generative artificial intelligence (GenAI) tools, by considering their use in relation to academic skill development. It focuses on a set of workshops conducted within a graduate professional development course at Queen’s University (Canada) in early 2024. The first workshop examined commonalities in Western, English academic writing structures; identified how demystifying these structures supports academic writing and reading practices; and considered how GenAI tools that utilise large language models (LLMs) mimic these structures to enhance students’ awareness of GenAI’s potential applications and limitations, and to identify the processes inherent in academic work. In the second workshop, students critiqued discipline-specific examples of AI-generated academic assignments. By exploring the qualities of academic writing alongside GenAI outputs, the workshop series invited students to explore the possibilities of what might be achieved through human-AI collaboration and to articulate what can never be replicated by a tool without embodied knowledge. This paper presented this set of workshops as a possible model for discussing GenAI tools with students—a model that demonstrates how GenAI tools might be integrated into students’ academic practices in ways that are ethical and effortful and which support, rather than stifle, student creativity.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".