Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond

Journal of University Teaching & Learning Practice Volume 20 Issue 2 Higher education and digital writing in a post-pandemic world Article 07 2023 Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond Mike Perkins British University Vietnam, Vietnam, Follow this and additional works at: https://ro.uow.edu.au/jutlp Recommended Citation Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2). https://doi.org/ 10.53761/1.20.02.07 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: Academic Integrity considerations of AI Large Language Models in the postpandemic era: ChatGPT and beyond Abstract This paper explores the academic integrity considerations of students’ use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond, including the teaching of writing and composition, the possibilities of co-creation between humans and AI, supporting EFL learners, and improving Automated Writing Evaluations (AWE). We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students. Analysing the various issues related to academic integrity that LLMs raise for both Higher Education Institutions (HEIs) and students, we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student. Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments. Practitioner Notes 1. Students now have easy access to advanced Artificial Intelligence based tools such as ChatGPT. These tools use Large Language Models (LLMs) and can be used to create original written content that students may use in their assessments. 2. These tools can be accessed using commercial services built on this software, often targeted to students as a means of ‘assisting’ students with assessments. 3. The output created by these LLMs is coherent enough for it not to be detected by academic staff members, or traditional text-matching software used to detect plagiarism, but falsified references may hint at their use if unchanged by students. 4. The use of these tools may not necessarily be considered as plagiarism if students are transparent in how they have been used in any submission, however it may be a breach of academic integrity policies of any given Higher Education Institution (HEI). 5. There are legitimate uses of these tools in supporting the education of students, meaning HEIs must carefully consider how policies dealing with student use of this software are created. Keywords Artificial Intelligence, Large Language Models, GPT-3, ChatGPT, plagiarism This article is available in Journal of University Teaching & Learning Practice: https://ro.uow.edu.au/jutlp/vol20/iss2/ 07 Perkins: Academic Integrity considerations of AI Large Language Models in Introduction During the COVID-19 pandemic Higher Education Institutions (HEIs) institutions worldwide were forced to rapidly alter the delivery and assessment of programmes traditionally taught and assessed in-person, as international restrictions on movement and gatherings prevented programmes from being delivered as planned (Kaqinari et al., 2021). This rapid transition to online learning meant that students were faced with entirely new assessment situations, and everchanging regulations both from HEIs, and from their respective governments. At the same time, HEIs were being faced with the challenge of attempting to maintain academic integrity to assure the quality and standards of their degrees (Clarke et al., 2022; Rapanta et al., 2021) while using alternative, and often novel, modes of assessment. Although online learning does not necessarily equate to higher amounts of academic misconduct occurring amongst students (Grijalva et al., 2006; Stuber-McEwen et al., 2009) online assessment has been shown to be associated with increased risks to academic integrity (Miller & YoungJones, 2012; St-Onge et al., 2022), as well as more cases of academic dishonesty occurring (Clarke et al., 2022; Lanier, 2006; Watson & Sottile, 2010). The particular situation of the pandemic has also resulted in a unique set of circumstances which has been demonstrated to lead to both an increase in detected cases of AD (Henderson et al., 2022; Jenkins et al., 2022; Lancaster & Cotarlan, 2021), as well as increases in student or academic staff perceptions of AD (Amzalag et al., 2021; Reedy et al., 2021; Walsh et al., 2021) occurring. During the period of the pandemic, research has shown that students sought out and adapted to new technologies (Vargo et al., 2021) as they were faced with large scale disruptions to their educational experience. As we emerge into a post-pandemic situation of learning, writing, and assessment, the availability of new digital tools is increasing the options that students have available to them in supporting assessments involving digital writing. This paper will explore one of these still-developing technologies that can enable new opportunities in digital writing, but also raises significant concerns related to academic integrity: Artificial Intelligence (AI) tools using Large Language Models (LLMs). This paper delves into the evolution of AI based digital tools and the emergence of LLMs and discusses several key areas to better understand LLMs, the key ethical concerns related to them, and the future of their use in digital writing and beyond. Firstly, we describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid traditional methods of detection by textmatching software. Secondly, we evaluate whether the use of LLM based tools to support students in writing assignments can be considered as plagiarism, academic misconduct, or a breach of academic integrity. Thirdly, we identify the potential these tools have for supporting the education of students, and whether academic staff can detect any such use of Academic Editors Academic Editors Section: Special Issue Section: Senior Editor: Dr Jo-Ann Kelder Senior Editor: Dr [First Last] Johinke Guest Editor: Rebecca Associate Editor: [First Last] Pu (...truncated)