This text aims to contribute to the discussion about how technology affects plagiarism and cheating in higher education. This is done by describing a process where researchers and one AI-bot interacted via text on the topic of plagiarism and cheating in higher education. By entering text data and interacting in multiple steps with the Chatbot ChatGPT [1], developed by OpenAI and trained by reinforcement learning, text was iterated in response to questions about cheating and plagiarism in exams. By testing the AI bot’s ability to produce text and discussing what input data we provide the bot with, we explore possible consequences and impacts on learning in higher education. The general understanding of a higher education institution’s approach to plagiarism is guided by a few principles. For illustration, Södertörn University communicates a document [2] that outlines rules and processes for examinations. Relevant to this are a couple of premises that were assumed to be true, at least until recently: A student could misuse another person's work by claiming the authorship, i.e., by not indicating a proper source, the submitted work is falsely presented as an original creation. Another form of academic cheating is outsourcing to and paying for another (sometimes academically qualified expert), a process and questionable practice known as procurement and criticized as a major institutionalized fauxthentication in higher education [3].  Currently, the cloud services check written assignments automatically for plagiarism function as fear of detection and then punitive deterrence. This contrasts with using these as selfevaluation tools, where feedback from the software can be used by lecturing staff to teach students about academic reference management and make knowledge claims regarding the discipline's literacy [4]. As pointed out by Hass [4] it is a problem if we do not train students on how to interpret the results of plagiarism detection programs. The digital tools available will invariably provide students with the capacity to utilize them to complement their effort in producing academic writing. We already know that many students use online search engines to search for texts, as well as apply programs for translation. Using these services in combination allows you to search for text on a specific topic and then copy and paste the text into the translation program. By switching between different languages, the text is "washed" to be experienced as an original contribution.  With the latest development of AI technology, we see further challenges when it comes to working with originality in the writing process. Let's look back on our interaction with the chatbot. By using the bot’s functionality, based on reinforcement learning, and questions about plagiarism in higher education, the chatbot presented the following as one consequence "...students may become overly reliant on AIbased tools and lose the ability to think critically and independently." [1]. The chatbot's output 54 is in line with previous research that problematized the use of AI tools for plagiarism, see for example Francke [5] in which ethical implications are highlighted. As academics schooled in the tradition of original writing and not-AI-aided scholars we are literally being technology-pushed into an “interesting” situation: We want to uphold the creativity and developing aspects of writing as an externalization of thinking, but without becoming AI-Luddites. Instead, we (fore-)see the need of discussing the effects and needed higher education adaptations and changes of these new and empowering tools to teach aspiring students how to write better academic texts with them, too.  

References: [1] OpenAI (2022). ChatGPT. online: https://openai.com/blog/chatgpt/ (2022-12-10) [2] Södertörns University (2022). Rights and obligations - rules for studying at the first and second- cycle level at Södertörn University. https://www.sh.se/english/sodertorn-university/student/opportunities-resources-and-rights/rightsobligations-and-safety/regulations-for-studying-at-bachelors-and-masters-level-first-and-secondcycle-at-sodertorn-university (2022-12-10) [3] Szyber, B., (2020). Fauxthentication: Art Academia and Authorship (or the site-specifics of the Academic Artist). Stockholm University of the Arts. [4] Hass, A. (2022). Using plagiarism feedback as assessment for learning to socialize students into  disciplinary writing: a theoretical perspective. South African Journal of Higher Education, 36(2), 133-150. [5] Francke, E., & Alexander, B. (2019). The potential influence of artificial intelligence on plagiarism a higher education perspective. In Proc. European Conference on the Impact of Artificial Intelligence and Robotics. EM Normandie Business School, Oxford (pp. 131-140).