AI Generated Code Plagiarism Detection in Computer Science Courses: A Literature Mapping

This is a full research paper. Integrity in the detection of plagiarism in students' source codes in university programming courses is a research topic for instructors and institutions seeking to improve the quality of their teaching. In particular, introductory courses such as CS1, are of paramount importance, as this is when students gain fundamental knowledge to build their future on. With the latest developments in Large Language Models (LLM) such as ChatGPT, GitHub Copilot, etc., methods of plagiarism have evolved, however methods of detection may not be capable of accurately differentiating between code generated by human and artificial intelligence (AI). In this context, this paper seeks to answer the research question: What does the current literature report on AI generated code plagiarism detection in higher education? To expand on and formulate a comprehensive answer to our research question (RQ), we have formulated six sub-questions: RQ1) How many papers were published per year by country?; RQ2) Which conferences and journals have published most papers on this subject?; RQ3) Which plagiarism detection tools were most often used prior to common AI use?; RQ4) How are educators adapting assignments to minimize the use of AI?; RQ5) Which modern methods are being deployed to specifically detect AI?; RQ6) Which data sources and languages are most prevalent in the literature? The methodology was based on a systematic literature review. Initially, we confined our search for literature to Scopus and Web of Science, however additional literature was included from Google Scholar. Inclusion criteria were applied to include documents from the years 2023 and 2024 (after the launch of ChatGPT), and only published by conferences and journals. Exclusion criteria: papers that do not focus on plagiarism and programming courses; papers that are not about the undergraduate-level; papers not written in English. We found 165 papers via Scopus and WebScience, from which the metadata were collected, resulting in 17 relevant papers selected for this work. The second step was a search in Google Scholar, where we analyzed 200 documents from 2023 (100 relevant documents) and 2024 (100 relevant documents). We used the same inclusion and exclusion criteria, however, we included the ArXiv papers, and found 9 more papers. Following this process, we have identified 26 papers to include in this literary mapping. In this paper we present the answers to these research questions and discussions about this research topic.