Ethical and effortful: workshopping human and generative AI academic writing collaborations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it