This paper reports on how undergraduate students evaluated writing outputs created with and without generative artificial intelligence (AI). The paper focuses specifically on two aspects of writing and AI: how prior writing knowledge influenced students’ thinking about AI tools, and how the writing skills to which they were exposed in the writing classroom helped them work with AI-generated materials. This research builds upon Bearman et al.’s (2024) work on evaluative judgement as a pedagogical tool to support learners as they work with AI-mediated texts. The paper uses this lens to identify challenges that learners have in applying writing knowledge to AI-mediated situations and to devise pedagogical means to support student learning in these contexts. We found that, while students could typically evaluate structural components of writing, they struggled to evaluate conceptual ideas both for AI and human generated texts. The findings speak more generally to the need for students to develop their evaluative abilities, as well as ways that AI may reveal and amplify existing challenges that learners have with evaluating the quality of writing, engaging with source materials, and applying genre knowledge to create meaning.

Academic writing has always posed a challenge to university students, regardless of the language they are writing in (first, second or foreign language) or the amount of digital support they have access to – for example, online dictionaries, thesauruses, or new generative artificial intelligence (GenAI) software such as ChatGPT. With the rise of GenAI as a legitimate digital tool in higher education, it is crucial to identify the professional development needs of teaching faculty in order to ensure quality teaching. Based on factors such as digital literacy, or access to digital tools, these needs might differ in various geographical regions. Within the context of the European Framework for the Digital Competence of Educators (DigCompEdu), this paper aims to provide a differentiated, international student perspective on the use of GenAI in the academic writing process, identifying professional development needs for faculty. We developed an online questionnaire that was filled out by 192 university students from 15 different countries. In addition to their academic and linguistic backgrounds, the respondents answered questions about their own experiences and competences with the use of GenAI within academic research. Results highlight clear discrepancies between geographic regions, for example, in their self-ranked digital proficiency or in what GenAI tools they use. This, along with further results from the analysis, provides the basis to identify some professional development needs.

GenAI has demonstrated functionality that seems, uncannily, to parallel reading and writing by identifying/reformulating information from source texts and generating novel content and argumentation. These skills are essential yet challenging for many students tasked with producing literature reviews. This study takes the first steps to investigating the feasibility of a GenAI-facilitated literature review. This investigation starts from the ‘human-in-the-loop’ position that complex processes can be deconstructed and compartmentalized, and that component functions needed for these processes can be delegated to machines while humans contribute to, or control, the overall process. We explore the hypothesis that certain functions of the literature review process, such as information extraction and content classification, might be able to be automated. Prompts modeled on recommended practices for research synthesis were designed to identify and classify particular types of content in research articles. Outputs produced by two GenAI models, GPT-3.5 and GPT-4o, were assessed for reliability with a human coder. Overall, the results posit concerns about the models’ performance on this task, cautioning against direct uses of GenAI output as learning scaffolding for students developing literature review skills.

The article presents results from a survey about the academic writing practices among the students of the University of Tartu (Estonia). We analyse how the use of generative artificial intelligence has changed between spring 2023 and spring 2024. Our data shows that there has been a small increase in the percentage of students who have used the help of AI while writing: in 2023, 43.9% of the students reported using or having used AI, in 2024 it was 51.6%. AI is most popular among the students of Science and Technology and least popular among the students of Humanities. In 2023, using AI was more common among undergraduates than master’s students, but by 2024 this situation had reversed. Among the activities that students use AI for, gathering ideas is most popular in both years. The biggest change between the two years is that the number of students using AI for summaries and overviews has nearly tripled. The paper discusses the possible reasons for these tendencies, as well as some relevant implications for learning and teaching (academic) writing.

The proliferation of AI tools for text editing and generation has raised challenging but also interesting questions for writing classes. In this paper, we report on our experiences with an exercise exploring the use of AI in an academic writing class. We first outline our conceptualization of the writing process, breaking down the skills that students need to master the complex task of writing, visualized as a ‘writing pie’. This breakdown allows us to critically assess the capabilities of AI tools against our understanding of writing as a human process. We then share our experiences with an exercise with ChatGPT in an academic writing class, where students evaluate a text with respect to its academic style and suggest improvements. Students then compare their own suggestions to those made by ChatGPT and critically evaluate the output. We include both instructors’ and students’ evaluations to reflect on whether the inclusion of such exercises can aid in achieving the course’s learning outcomes. We share three key takeaways: (1) there is value to having students work with AI; (2) critical evaluation of AI output is key; (3) activities with AI should be evaluated against learning goals.

This symposium is an extension of a plenary forum on generative AI (hereafter GenAI) held at the EATAW Conference at Zurich University of Applied Sciences in Winterthur, Switzerland, in June 2023. Since the conference, AI – particularly the large language models (LLMs) shaping GenAI such as OpenAI’s ChatGPT – continue to develop rapidly with extensive integration and usage across disciplines and career sectors with educational and societal impacts. Given these developments, we recognize the central role that writing instruction has in fostering critical literacies and engaged usage and, at times, non-usage of GenAI. Just as we have adapted our teaching and learning to other technological developments, so too are we now at a time of transition and adaptation. Our initial discussion at EATAW was wide-ranging, intentionally so because (1) there is so much to explore in relation to GenAI, and (2) the EATAW membership is diverse, coming from a range of academic backgrounds. Thus in our original plenary and here in this symposium we have raised issues ranging from specific pedagogical approaches to questions of program and institutional administration, to broader public issues and conversations about the relationship of humans to machines. Here in this written symposium we each raise a different issue related to GenAI and writing with the aim to foster dialogue and discussion about GenAI in writing-related contexts.

When students submit written assignments for assessment, they are generally trusted to have completed these honestly, and to have benefitted from the opportunity to learn. Academic integrity breaches are sometimes detected during the assessment process. Some common examples of integrity breaches during students’ academic writing include contract cheating, the unauthorised use of GenAI technology for completing assignments, and using AI tools to disguise existing work so that it appears to be original. None of these are new phenomena. Processes and procedures should be in place for managing suspected academic misconduct cases detected during the assessment process. But what happens when academic misconduct is detected retrospectively, sometimes after a student has moved degree programmes or graduated? This position paper sets out the case for universities and other academic institutions having procedures in place to deal with historic academic misconduct. It provides examples of how institutions can become aware of misconduct, including through whistleblowing and through development of more effective detection software. The authors bring together legal and educational expertise to suggest considerations that individual institutions should make towards future policy development. The discussion considers that students must be supported and prepared for success, but that institutions cannot ignore the reputational risks associated with cases of historic misconduct.

This paper recommends that explicit value be placed on promoting dialogue among staff and students with respect to academic integrity in higher education. A detailed literature review revealed a notable lack of literature on resources and practices for professional development of staff on academic integrity or the importance of engaging academic staff in such training. Through the authors’ experience in developing and facilitating workshops, they have designed a flexible approach to academic integrity professional development for academic staff that highlights the importance of discussion and communication. Throughout this workshop development, scenarios were created to prompt discussion on a wide range of academic integrity issues (including Generative Artificial Intelligence (GenAI)). In total, 18 workshops addressing academic integrity have been run by the authors and attended by 180 staff and 85 students at local, national, and international levels. This experience-based paper situates the need for professional development on academic integrity within the current literature and shares the evolution of the authors’ training workshops and resource development. Readers are encouraged to use the resources in their own contexts to prompt dialogue within their institutions on academic integrity.

This teaching practice paper shows how students may choose to work with ChatGPT, generative AI and Large Language Models (LLMs) to produce essays and written assessment solutions in a manner that may be considered as either acceptable or as a breach of academic integrity depending on individual and institutional views. Following a brief introduction to how chatbots work, case study examples show how modified prompts can be used to generate writing in alternative styles, how a writing tutor review can be simulated, and how LLMs can be run locally and without Internet access. The paper is intended to inform academic writing tutors, instructors, and assessors what is possible using generative AI for writing as of January 2024. It is not positioned to make a judgement regarding what is acceptable, but rather to illustrate how technically proficient users can accomplish more than is often indicated by writing beginner level prompts for a chatbot. Such techniques are accessible to many students and the Academic Writing Development community will need to consider its response.

The emergence of generative Artificial Intelligence (GenAI) tools such as ChatGPT has attracted wide attention in the field of L2 writing and academic writing, but few papers to date have analysed GenAI’s potential application (positive and negative) in source use practices in academic writing. This article discusses three key aspects of source use – academic attribution, searching and reading sources, and source integration. AI tools are trialled for each aspect, followed by an overall SWOT analysis. While writers can use AI tools to assist on several source use practices, they are not recommended to use AI without a deep understanding of academic writing and source use principles. This article concludes with suggestions for student writers, academic support providers, and institutions.

In this teaching practice article, we discuss the possibilities of integrating AI into the writing classroom utilizing prompt engineering techniques. We propose a strategy for prompt engineering in which we see AI as an audience and interlocutor during the invention process. We consider using the method in preparation for argument composition and with that we propose an ethical model for teaching writing based on a view of rhetoric as both technê and praxis. To draw attention to the ethical question in relation to human—non-human interactions, we use as metaphor for AI tools the image of Klara, an android who serves as a children’s companion in Ishiguro’s novel Klara and the Sun (2021).

AI-based natural language production systems are currently able to produce unique text with minimal human intervention. Because such systems are improving at a very fast pace, teachers who expect students to produce their own writing—engaging in the complex processes of generating and organizing ideas, researching topics, drafting coherent prose, and using feedback to make principled revisions that both improve the quality of the text and help them to develop as writers—will confront the prospect that students can use the systems to produce human-looking text without engaging in these processes. In this article, we first describe the nature and capabilities of AI-based natural language production systems such as GPT-3, then offer some suggestions for how instructors might meet the challenges of the increasing improvement of the systems and their availability to students.