The emergence and popularity of generative artificial intelligence (AI) tools, particularly text-based ones known as large language models, pose both opportunities and challenges to education. The ability of these tools to generate human-like texts based on minimal instructions causes concerns among educators about students’ use of these tools for academic writing, which may constitute a breach of academic integrity. We propose a pedagogical design that models on self-regulated learning and the authoring cycle and develops students’ critical thinking and self-regulation when composing academic writing using text-based generative AI tools. It contains six iterative and interactive phases. Students first plan the content and structure of the writing, then generate prompts for text-based generative AI tools. Next, students preview and verify the tools’ output, followed by the fourth phase of producing the writing using the corrected output. Fifthly, peer review by fellow students may be required to polish and proofread the writing. Lastly, through portfolio-tracking, students reflect on the writing process, and formulate strategies for future usage of text-based generative AI tools for writing. This pedagogical design helps students and teachers embrace text-based generative AI while addressing the perils these tools present, and guides the development of education interventions and instruments. A pedagogical design for self-regulated learning in academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of plan, prompt, preview, produce, peer-review, portfolio-tracking Siu-Cheung Kong 1 *, John Chi-Kin Lee 2 and Olson Tsang 3 *Correspondence: sckong@eduhk.hk siucheungkong@gmail.com Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR Full list of author information is available at the end of the article Abstract The emergence and popularity of generative artificial intelligence (AI) tools, particularly text-based ones known as large language models, pose both opportunities and challenges to education. The ability of these tools to generate human-like texts based on minimal instructions causes concerns among educators about students’ use of these tools for academic writing, which may constitute a breach of academic integrity. We propose a pedagogical design that models on self - regulated learning and the authoring cycle and develops students’ critical thinking and self-regulation when composing academic writing using text-based generative AI tools. It contains six iterative and interactive phases. Students first plan the content and structure of the writing, then generate prompts for text -based generative AI tools. Keywords: 6-P pedagogy, Academic writing, Artificial intelligence literacy, ChatGPT, Critical thinking, Pedagogical design, Self-regulated learning Kong et al. Research and Practice in Technology Enhanced Learning (2024) 19:30 Page 2 of 18 Introduction The year 2022 has seen the release of a number of generative artificial intelligence (AI) algorithms and models that are able to generate images, videos, texts and sound (Peres et al., 2023). In particular, thanks to its ability to generate diverse forms o f original, human- like texts, the text -based generative AI tool ChatGPT (which stands for Chat Generative Pre-trained Transformer) has become the most popular and influential. To avoid plagiarism, students are required to digest the information from various AI and non-AI sources and synthesise the information into a coherent piece of writing (Dwivedi et al., 2023). That students are the one producing the writing also emphasises that they are responsible for ensuring the credibility of the content, in accordance with the ethical principle of human autonomy in AI usage (Kaur et al., 202 2). The education community agrees that there is an urgent need to develop students’ awareness in and attitudes to academic integrity when using text-based generative AI tools for academic writing (Cotton et al., 2023; Dwivedi et al., 2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. Advance online publication. https://doi.org/10.1080/14703297.2023.2190148 Crawford, J., Cowling, M., & Allen, K. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3), 2, 1–19. https://doi.org/10.53761/1.20.3.02 Desaire, H., Chua, A. E., Isom, M., Jarosova, M., & Hua, D. (2023). Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools. https://doi.org/10.1177/00336882231162868 Kong, S.-C. (2014). Developing information literacy and critical thinking skills through domain knowledge learning in digital classrooms: An experience of practicing flipped classroom strategy. Computers and Education, 78, 160– 173. https://doi.org/10.1016/j.compedu.2014.05.009 Kong, S.-C., & Lee, J. C.-K. (2023). A proposed pedagogical approach for academic writing using artificial intelligence- enabled text generating tools: 6-P Pedagogy of Plan, Prompt, Preview, Produce, Peer-Review, Portfolio-Tracking [Working Paper]. The Education University of Hong Kong. https://www.lttc.eduhk.hk/papers/6p Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026 Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198, 1–12.

the impact of artificial intelligence on writing and plagiarism. One such innovation is the GPT-2 model, which is capable of generating coherent paragraphs Plagiarism is the representation of another person's language, thoughts, ideas, or expressions as one's own original work. Although precise definitions vary depending on the institution, in many countries and cultures plagiarism is considered a violation of academic integrity and journalistic ethics, as well as of social norms around learning, teaching, research, fairness, respect, and responsibil Turnitin, an internet-based plagiarism detection service, emerged as a digital platform in 1995 and quickly dominated the market. Turnitin serves more than 30 million students worldwide across over 10,000 institutions in 135 countries, and has been utilized by over 1.6 million instructors. When evaluating an article, Turnitin uses artificial intelligence to generate both formative and summative assessments. The formative assessment is meant to provide instructors with a basic evaluation of the student's level of achievement, while the summative assessment represents its final evaluative judgment of the writing. According to Turnitin, the "Turnitin Scoring Engine" generating these mainly focuses on analyzing patterns in previously evaluated essays. It also rates the readability of content and the writer's familiarity with the genre, based on an evaluation of word usage, genre conventions, and sentence structure. The final report page highlights potentially plagiarised sections to help instructors identify the corresponding content. Despite its technological advancements, Turnitin has some limitations. A Croatian study found that "small"-language (languages with less of a digital footprint) written material is not supported by the larger base of plagiarism-detection tools, and that languages with more of a digital footprint and more outreach tend to be better supported. The generation of reports by Turnitin, which involves comparing and scoring vast amounts of student work, can potentially infringe on copyright laws. Turnitin monitors students to ensure that their work is original and unique, with this validation process being carried out by a supervising machine. However, this practice can result in unrestricted access to student data for teachers, institutions, and governments and lead to severe copyright infringement issues. Furthermore, plagiarism detection systems (PDS), especially when used for grading purposes, have certain drawbacks. While Turnitin can identify matching texts, it does not provide a clear definition of plagiarism, leaving potential disputes for individual interpretation. For example, different instructors may interpret the same report with…