Accessing the Epistemological Side of Writing: A Prolegomenon to the Era of AI

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Accessing the Epistemological Side of Writing: A Prolegomenon to the Era of AI

Type
Symposium
Discussant
Schnatz, Nina Isabella , Zurich University of Applied Sciences (ZHAW), Switzerland
Keywords
academic writing;AI writing;corpus linguistics;Generative AI
Domain
Learning and Instructional Technology

Abstract

Traditionally, the study of writing has focused on rhetorical, linguistic, cultural, social, and process-related dimensions. The epistemological side of writing, however, has been left to the disciplines as they oversee their respective fields of knowledge. Rarely do we directly consider students’ conceptions of truth and their understanding of knowledge or knowing in the way William Perry (1970) has addressed it. Even if academic writing may be seen as the best way of developing epistemic consciousness, the term itself not often the focus of research, and the broad range of intellectual skills behind it remains hidden. We are aware, however, that every topical sentence demands complex judgements about its appropriateness and needs justification of its assumed truth. Such epistemic activities demand understanding of what is considered valid knowledge, how it is produced, what epistemic conventions exist, and how epistemic authority is built in a certain discipline. Beyond all this, the conception of truth is a nut that is hard to crack, not only for our students but also for philosophy. We are used to confusing our students by insisting that they rely on facts but should not believe in absolute truth. How do these two demands go together?With the inclusion of generative AI in writing processes, a new algorithmic “voice” enters the field that also requires epistemic framing. However, this voice has different qualities and shortcomings compared to human epistemological consciousness during writing. Writers must evaluate their own thoughts against the AI-generated content, which presents new challenges, particularly for beginners.This symposium introduces the concept of epistemic consciousness in writing. It presents several methodological approaches, manifested in four specific research projects, to bring to the surface epistemic processes involved in academic writing. Presenters will explain the logic of the enquiry in each project along with some initial results. We intend for the symposium to stimulate new avenues for research, contributing to the exploration of human–AI interaction in writing and thinking.Project 1: Qualitative InterviewsProject 2: How Expert and Novice Academics Write with GenAI: Think-Aloud ProtocolsProject 3: Corpus Linguistic Methods

A Direct Approach to the Study of Epistemic Decisions: Students Using AI for Thesis Writing

Keywords
academic writing;AI writing;cognitive processes;Generative AI
Domain
-

Abstract

Understanding how students make epistemic decisions when using AI technologies for academic writing requires methodological approaches that can capture the nuanced intellectual and rhetorical processes underlying their choices. While existing research has documented patterns of AI adoption and usage frequencies, there remains a significant gap in our understanding of the detailed thinking processes that guide students' decisions about when, how, and why to incorporate AI-generated content into their scholarly work. This study addresses this methodological challenge through a qualitative interview-based approach designed to access students' reflective accounts of their AI use experiences during thesis writing. As a contribution to get methodological access to AI use, this contribution reports from a larger study including three countries (Switzerland, Romania, Bulgaria) to interview students about their experiences with AI. The cross-national design allows for comparative insights into how different educational contexts and cultural backgrounds may shape students' approaches to AI integration in academic writing. The background problem of this is that we currently have many surface descriptions about AI use, but little understanding of the finer-grained thinking moves involved. Existing survey and usage data tell us what students do with AI, but not how they think through the complex decisions about knowledge construction, source integration, and authorial voice that AI use entails. Pilot interviews have been conducted with undergraduate and graduate students currently writing their theses. The interview protocol focuses on eliciting detailed narratives about specific instances of AI use, prompting students to articulate their decision-making processes, and exploring their conceptions of authorship, originality, and epistemic development in AI-assisted writing contexts. We will describe the questions that proved to be useful and summarize our experiences with this direct way of questioning students. Key results will be presented along with recommendations for interview strategies that successfully access students' epistemic reasoning in AI-assisted thesis writing.

Corpus Linguistic Methods

Keywords
academic writing;corpus linguistics;reading and writing;writing and linguistics
Domain
-

Abstract

This study employs corpus linguistic methods to systematically investigate the linguistic and epistemic dimensions of academic thesis writing. Through the compilation and analysis of a specialized corpus of BA theses, the research seeks to identify patterns in how students construct knowledge claims and position themselves within their academic field.  The epistemic profiles of the students will be assessed through these focused corpora of BA theses. The corpora will be compiled from successfully defended theses across pre-selected disciplines, providing a representative sample of academic writing practices. The fact that the thesis writing has been guided by academic tutors in the respective area, ensures that the analyzed texts have undergone rigorous evaluation and represent successful models of scholarly argumentation as well as sufficient knowledge presentation of the topic from a BA-level perspective.  On the one hand, various linguistic indicators will be discussed with respect to their frequency, variance, and syntagmatic adequacy, such as hedges, modal expressions, markers of cohesion and coherence, linking words, references to reviewed literature, etc. The analysis will examine how these features pattern across different texts and authors, revealing underlying epistemic orientations and rhetorical strategies. Hedges and modal expressions, for instance, indicate how writers negotiate certainty and manage knowledge claims, while cohesion and coherence markers demonstrate how arguments are structured and connected throughout the overall thesis text. By analyzing frequency distributions and contextual deployment of these features, the study will identify the academic conventions and the individual variation in the epistemic positioning of the student.  On the other hand, the role of language corpora will be considered for ensuring better data extraction and observation in the analytical part of the thesis. Here also the inclusion of AI as a stand-alone tool, or in combined architectures with corpus search engines will be presented. This methodological approach explores how AI technologies can enhance traditional corpus linguistic methods, potentially offering new possibilities for pattern recognition and analytical depth in examining academic discourse.

How Expert and Novice Academics Write with GenAI: Think-Aloud Protocols

Keywords
cognitive processes;Generative AI;metacognition;protocol analysis
Domain
-

Abstract

Two related studies aim to track the infusion of GenAI into knowledge generation and diffusion processes among expert and novice academic writers across disciplines working on authentic revision tasks in writing. The first study examines experienced academic researchers and writers from diverse disciplinary backgrounds, including humanities, social sciences, and STEM fields. Using Zoom-based think-aloud methods along with keyboard tracking, the study captures real-time data on writers' cognitive processes and writing behaviors as they interact with GenAI systems. The think-aloud protocols highlight the ways in which and the degrees to which GenAI influences experienced writers' metacognitive and revision processes, epistemic development, and agency across domains of knowledge (Tardy, 2009; Kessler et al., 2026). By focusing on authentic revision tasks rather than artificial laboratory settings, the research ensures ecological validity and provides insights into actual scholarly practices. Results indicate the ways in which today's highly effective thinkers and knowledge producers incorporate (or don't) GenAI into their research and research writing practices. In the second study, undergraduate students used ChatGPT to assist them in writing 100-word literacy narratives focusing on a specific moment in their literate history. They then revised the output based on how effectively it captured their rhetorical, stylistic, and content-related intentions. Their entire process was recorded using screencast technology as they spoke their processes aloud. After finalizingtheir texts, they wrote a brief reflection on the experience. This contribution will present a thematic and code-based analysis of the epistemic decisions students made in their revisions of the outputs, with implications for reforming methods for supporting writing in the age of generative AI. Taken together, the two studies reveal differences between the epistemic processes of experienced and novice writers and suggest a developmental continuum for instruction in the use of generative AI in writing tasks.