Trusting Each Other, Trusting Machines: Undergraduate Students’ Perceptions of Copresence Afforded by Writing Technologies, Networked Platforms, and Generative AI in Their Academic Writing Practices

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.

Authors

Institutions

Publication Details

Journal
Writing & Pedagogy
Published
2025-12-01
DOI
https://doi.org/10.3138/wap-2025-0004
Primary Topic
Writing and Handwriting Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Trusting Each Other, Trusting Machines: Undergraduate Students’ Perceptions of Copresence Afforded by Writing Technologies, Networked Platforms, and Generative AI in Their Academic Writing Practices

Carl Whithaus, Kate Maddalena, Tracey Bowen
Writing & Pedagogy
Writing and Handwriting Education
article

Trusting Each Other, Trusting Machines: Undergraduate Students’ Perceptions of Copresence Afforded by Writing Technologies, Networked Platforms, and Generative AI in Their Academic Writing Practices

Carl Whithaus, Kate Maddalena, Tracey Bowen
article en

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.

Writing & PedagogyVol. 16(2)
University of Toronto (CA), University of California, Davis (US)
Quality Education
Openalex Percentile: Top 19%
Writing and Handwriting Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.