Year: 2026
Type: dissertation
Abstract: In higher education, writing plays an important role in supporting students’ learning and critical thinking across disciplines and contexts. However, despite its essential role in supporting undergraduate students’ learning and success beyond the classroom, writing is difficult for many students to master and challenging for disciplinary teachers to incorporate effectively while still accomplishing primary course learning objectives. Generative AI writing tools could serve as reliable writing resources external to the teacher that support students’ writing development in and beyond the writing classroom. These tools offer important affordances for promoting students’ critical thinking and writing skills as well as scaffolding parts of the writing process; however, they also have serious biases and limitations that must be considered for use in the classroom. Due to the increasing speed at which generative AI tools are developed and made available for public use, further research into the ways in which generative AI may support and hinder undergraduate students’ writing development is essential for understanding how to best prepare students for the professional contexts in which they will write. This dissertation, driven by a qualitative, multiple case study approach, examines the ways in which teachers and students engage with generative AI in the undergraduate, upper-division writing classroom. In particular, this dissertation focuses on teachers’ implementation of AI to support the learning goals of their classroom as contextualized in their pre-existing pedagogical practices and beliefs about writing pedagogy. In addition, by characterizing and x observing the writing communities formed in the writing classrooms under study, this dissertation explores the ways in which generative AI may shape the development of the writing community. This project is grounded in the framework presented by the writers-within-community (WWC) model (Graham, 2018), specifically focusing on the organizing structure of the components of writing communities as well as the beliefs held by community members related to writing and AI as a means of understanding the relationships between community members, tools, and other internal and external factors that shape the development of the community. Findings categorize teachers’ implementation of AI through three primary approaches: AI as a tool, a partner, or a threat. Teachers’ implementations of AI appear to be driven primarily by teachers’ pre-existing pedagogical practices and objectives as well as their held beliefs about writing and generative AI. Specifically, teachers who chose to leverage generative AI to enhance their writing pedagogy viewed the AI as aligning with their approaches to fostering writing agency, whereas teachers who chose to limit their implementation of generative AI viewed the AI as detrimental to students’ development of writing agency. Students largely used AI in ways that reflected their teacher’s specific implementation choices as well as their prior beliefs about writing and generative AI; their receptiveness to their teacher’s implementation of AI appeared to be grounded in the degree of trust they had in their teacher to support their development as writers in the engineering field. The role of generative AI in each writing community was largely determined by teachers’ implementation choices, though the role AI played in community writing interactions was not static. Rather, the AI tool took on different writing roles as directed by the particular pedagogical needs of teachers and writing needs of students, capable of fulfilling writing roles traditionally occupied by teachers and students in the writing classroom while fundamentally not being a member of the community itself. The dissertation concludes by proposing a revision to the WWC model to accommodate the shifting and adaptable presence of generative AI tools in the community as well as considering implications of these findings for practice and research.
Author: Tseng, Waverly
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7171475737