Trusting Each Other, Trusting Machines: Undergraduate Students’ Perceptions of Copresence Afforded by Writing Technologies, Networked Platforms, and Generative AI in Their Academic Writing Practices
Bibliographic record
Abstract
This article examines how students use and perceive digital writing tools, including chat platforms and generative AI, within academic writing environments. It describes a qualitative study of 15 undergraduate students in guided focus group discussions. In a grounded theory analysis of focus group transcripts, the researchers explored undergraduates’ sense of copresence—their perception of support through both human interaction with both peers and instructors and AI technologies during their writing processes. Findings reveal that students’ trust in both peer feedback and AI assistance plays a crucial role in their writing, shaping their decisions about which tools to use and how they integrate human and AI feedback in the development and revisions of their writing. The study sheds light on students’ nuanced understanding of the affordances and limitations of multimodal chat platforms and generative AI technologies. We conclude by highlighting the need for pedagogical practices that support students’ choice of tools when collaborating in digital spaces. We suggest future research directions that will enable us to better understand how copresence and trust influence students’ writing in these contexts.
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 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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".