The future of writing education

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The future of writing education

Type
Symposium
Keywords
AI writing;assessment;Generative AI;writing instruction
Domain
Learning and Instructional Technology

Abstract

Writing has long been a cornerstone of education, serving both as a means of learning and as a key indicator of students’ understanding, reasoning, and communicative competence. Today, this foundational role is being challenged by the rapid emergence of generative artificial intelligence. From compulsory education to higher education, generative AI tools are increasingly influencing how learners engage with writing tasks, raising fundamental questions about authorship, originality, assessment, and the purposes of writing instruction itself. Rather than signaling the end of writing education, these developments invite a critical rethinking of writing education in an AI-rich educational landscape.This symposium brings together three research studies that collectively examine current developments in writing education in contexts where generative AI is increasingly embedded in educational practice. The first paper examines teachers’ detection of AI-generated text by exploring which textual features teachers associate with students’ use of generative AI. Drawing on survey data from Norwegian teachers, the study analyses how teachers interpret student writing and make judgments about authorship in contexts where generative AI is increasingly present. The second paper shifts attention from writing products to writing processes by examining how students’ interactions with generative AI can be used to inform the assessment of argumentative writing. It explores the potential of process data, such as prompts, revisions, and AI-mediated decision-making, as complementary evidence in writing assessment. The third paper focuses on higher education and investigates how generative AI can be integrated responsibly into students’ writing processes. It examines students’ existing uses of these tools and the role of instructional guidance in supporting critical, reflective, and autonomous writing practices.Taken together, the symposium offers a coherent and forward-looking view on the future of writing education, positioning generative AI not merely as a challenge, but as a resource that can inform and support writing processes.

Assessing argumentative writing through students’ interactions with generative AI

Keywords
AI writing;assessment;Generative AI;writing processes
Domain
Assessment and Evaluation

Abstract

As generative artificial intelligence (genAI) increasingly produces text that is indistinguishable from human work, conventional assessments that focus solely on the written product are becoming an unreliable measure of student learning. In this presentation, we therefore introduce an assessment method that focuses on the writing process. We focus on two components of student–genAI interaction during argumentative writing. First, directive reasoning interaction, which captures how purposefully students steer the AI. This is important because passive acceptance of AI output is often associated with lower-quality writing. Second, visible expertise, which reflects the extent to which course-related conceptual knowledge becomes apparent in the interactions.Student–genAI interaction data and final essay grades were collected from 70 graduate students who wrote argumentative essays using a self-chosen genAI tool. All 1,450 prompts were annotated using our taxonomy, developed from the course learning objectives combined with indicators of directive reasoning interaction and visible expertise. The taxonomy contains three main categories: writing, content, and argument, and 35 subcategories.The results showed that students most often prompted genAI to improve or evaluate their writing, such as grammar and style (41%). GenAI was used less frequently to evaluate or improve content (29%) or argumentation (22%). Interactions indicative of high directive reasoning interaction and visible expertise were positively related to performance. For example, prompts asking genAI to revise a specific argument, based on a clear, conceptual critique; or to integrate information from a source into a premise, were associated with higher essay grades. In contrast, interactions showing low directive reasoning or low visible expertise, such as “write an essay on topic X” or requesting a summary to be inserted into the essay, were related to below-average essay grades.To conclude, evaluating the writing process through student-genAI interactions may be used to complement and even replace traditional essay assessment methods. Future work should examine the generalizability of our findings to other argumentative writing assignments and explore how the assessment approach might apply to other types of written assessment. Finally, as genAI evolves, it needs to be considered whether any interactions from our taxonomy might become obsolete.

Text Features Associated with Students’ Generative AI Use: Norwegian Teachers’ Assessments

Keywords
AI writing;assessment;Generative AI;writing instruction
Domain
-

Abstract

The release of generative AI (genAI) tools has changed the way that many educators interact with student writing, as they grapple with assessing how students use this technology for writing and how their uses may support or detract from learning. This paper draws from a survey of 530 Norwegian teachers designed to examine teachers’ perspectives on genAI, including their uses of AI to teach writing, their beliefs and ethical concerns about students’ AI use for writing, their preparedness to use AI, and, the focus of the current paper, the text features they associate with students’ AI use. GenAI presents new challenges for teachers’ writing assessment practice as it complicates their construction of the student author. Although written communication as academic assignment is skewed toward language performance to be assessed (Smagorinsky et al., 2010), a key aspect of the assessment process involves teachers’ interpretations of what a student is working to express in writing. Given that human communication is co-constructed, “it must follow that even when we don’t know the person who generated the language we are interpreting, we build a partial model of who they are and what common ground we think they share with us, and use this in interpreting their words” (Bender et al., 2021, p. 616). Many teachers are compelled to consider the extent to which their model of “the person who generated the language” is genAI-mediated. This paper focusses on a qualitative content analysis of an open survey item in which a subset of 129 teachers shared their perceptions of the text features that signal students’ use of generative AI and their stances toward these text features. We analysed teachers’ responses to investigate how they adapt their writing assessment practices in the context of students’ genAI use. We found that teachers viewed AI-associated text characteristics negatively, and they focused on language features indicative of voice and style when identifying aspects of student text that suggested AI use. Our results suggest that teachers’ individualized knowledge of students’ development vis-a-vis academic writing tasks and subject-matter learning factors into their judgments of whether a text is student-composed or AI-generated.

Using Generative AI for Academic Writing: Students’ Practices and the Role of Explicit Instruction

Presenting author
Vereecke, Alix , Ghent University, Belgium
Keywords
academic writing;AI writing;Generative AI;writing instruction
Domain
Higher Education

Abstract

Generative AI tools such as ChatGPT are now widespread in higher education and are often presented as promisingforms of support for academic writing, a complex skill that many students find challenging. While concerns about misuse and authorship persist, considerably less is known about how students actually use generative AI during the writing process, or about whether instructional guidance can support more responsible and effective use.This study adopts a two-phase design. In the first phase, a questionnaire study with 170 higher education students examined whether and how students use generative AI during writing, focusing on self-reported moments of use and the specific aspects of the writing process targeted. In the second phase, a pilot intervention study with 20 students explored students’ actual AI use in greater depth by comparing writing processes with and without explicit instruction on responsible AI use and prompting. Data sources included students’ prompts, generative AI conversations, and final texts, which were analysed using quantitative content analysis and comparative judgement to assess changes in prompt quality, revision practices, and overall text quality.Results from the questionnaire show that the vast majority of students (92%) report using generative AI during the writing process. However, students tend to use these tools in a limited manner, primarily for relatively straightforward tasks such as correcting language and spelling or reformulating existing text, rather than for more substantive support throughout the writing process. Moreover, self-report data provided only limited insight into students’ responsible use of AI. Findings from the pilot intervention study suggest that, in the absence of instruction, students do not consistently engage with generative AI in a responsible manner. Following explicit instruction, students formulated significantly higher-quality prompts and interacted more critically with AI-generated output. Although text quality improved for all students, no significant difference was found between students who did and did not receive instruction.Overall, the findings suggest that although generative AI is already widely used in academic writing, responsible and effective use cannot be assumed. Brief, targeted instruction on prompting and responsible AI use may therefore play a key role in supporting more meaningful integration of generative AI into students’ writing processes.