AI or Student Writing? Analyzing the Situational and Linguistic Characteristics of Undergraduate Student Writing and AI-Generated Assignments

Jul 9, 2026

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Universities face the challenge of whether traditional written assignments still have value after the release of OpenAI’s ChatGPT and its ability to emulate multiple writing forms. This study evaluates how far AI-generated assignments can reproduce the situational and linguistic features of undergraduate, student-authored work in an English as a Foreign Language (EFL) context. A register-based corpus comparison contrasts student responses with ChatGPT outputs across essays, critiques, and personal narratives, applying Biber and Conrad’s (2019) framework of situational analysis, linguistic analysis, and functional interpretation to guide instructors in using AI tools.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages50

Reading Time2 h 6 min

Introduction

Research aim and motivation

Study design and comparison framework

Research questions

Montclair State University Montclair State University Digital Commons | Department of Linguistics Faculty Scholarship and Creative Works | Department of Linguistics | | --- | --- | | 12-1-2024 AI or student writing? Analyzing the situational and linguistic characteristics of undergraduate student writing and AI-generated assignments Larissa Goulart Montclair State University, [goulartl@montclair.edu](goulartl@montclair.edu) Marine Laísa Matte Instituto Federal de Educação, Ciência e Tecnologia Sul-rio-grandense (IFSul) Alanna Mendoza Montclair State University, [mendozaa15@montclair.edu](mendozaa15@montclair.edu) Lee Alvarado Montclair State University, [alvaradol4@montclair.edu](alvaradol4@montclair.edu)[ ](alvaradol4@montclair.edu)Ingrid Veloso Montclair State University Follow this and additional works at: [https://digitalcommons.montclair.edu/linguistics-facpubs](https://digitalcommons.montclair.edu/linguistics-facpubs) Part of the Applied Linguistics Commons | | MSU Digital Commons Citation Goulart, Larissa; Matte, Marine Laísa; Mendoza, Alanna; Alvarado, Lee; and Veloso, Ingrid, "AI or student writing? Analyzing the situational and linguistic characteristics of undergraduate student writing and AIgenerated assignments" (2024) . Department of Linguistics Faculty Scholarship and Creative Works. 55. [https://digitalcommons.montclair.edu/linguistics-facpubs/55](https://digitalcommons.montclair.edu/linguistics-facpubs/55) This Preprint is brought to you for free and open access by the Department of Linguistics at Montclair State University Digital Commons. It has been accepted for inclusion in Department of Linguistics Faculty Scholarship and Creative Works by an authorized administrator of Montclair State University Digital Commons. For more information, please contact [digitalcommons@montclair.edu](digitalcommons@montclair.edu). AI or Student Writing? Analyzing the situational and linguistic characteristics of Undergraduate Student Writing and AI-Generated Assignments Abstract: Since the release of OpenAI’s ChatGPT, universities have faced the issue of whether there is still a place for written assignments in higher education. ChatGPT's capacity to mimic various written forms raises questions about the necessity of traditional assessments. Given this background, this study explores to what extent AI-generated assignments can replicate the situational and linguistic features of student-authored assignments. Using a corpus of undergraduate assignments from an English as a Foreign Language (EFL) context, we compare student responses with ChatGPT's outputs. Employing a register approach, we analyze the situational and linguistic characteristics of texts across three different registers—essays, critiques, and personal narratives. Our methodology follows Biber and Conrad’s (2019) framework, encompassing situational analysis, linguistic analysis, and functional interpretation. The findings aim to inform writing instructors and EFL teachers about the strengths and limitations of AI tools, enhancing their ability to guide students in integrating these technologies into their writing processes. Keywords: University Writing, Register Studies, Generative AI, Multidimensional Analysis 1. Introduction Since the public release of OpenAI’s ChatGPT in November 2022, university stakeholders have grappled with the question of how ChatGPT can affect university assessment practices, for both short (e.g., essays, critiques, or lab reports), and long writing assignments (e.g., theses and dissertations) . ChatGPT’s ability to emulate different forms of written texts has put into question the need for these forms of assessment. From the point of view of instructors and professors, undergraduate student writing traditionally requires students to demonstrate knowledge of their subject matter (Gardner & Nesi, 2013) . However, with ChatGPT and other generative AI tools becoming widespread, t

FAQs

What problem does the study address regarding ChatGPT and university writing assessment?

The study examines whether ChatGPT’s ability to mimic different writing forms challenges the role of traditional written assignments in higher education.

How does the research compare AI-generated assignments with student writing?

It uses a corpus from an EFL undergraduate context and compares student-authored responses with ChatGPT outputs using a register approach.

What registers and linguistic/situational dimensions are analyzed?

The study analyzes essays, critiques, and personal narratives, assessing both situational characteristics (e.g., audience, author, purpose, mode, production circumstances) and linguistic features, followed by functional interpretation.

Jul 9, 2026

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