Informatics in Education

Given the emergence of GenAI, students should develop GenAI literacy to promote its benefits and mitigate its drawbacks. However, many studies focus on enhancing their learning experience with GenAI, not understanding GenAI literacy. Two studies are dedicated to GenAI literacy, but they either require additional sessions or focus on overly specific tasks. We integrate GenAI into a data science course and its assessments to specifically promote GenAI literacy for non-computing students. The course design expects students to learn from their direct experience with GenAI, especially regarding GenAI usability, reliability, ethics, and privacy. Students are encouraged to use and acknowledge GenAI for some assessments and to align GenAI-generated programs to their own styles. Our evaluation involving 113 students showed that the course design might help students to understand GenAI characteristics and change their behaviour. Students are unlikely to be involved in GenAI misuse. Further, they align GenAI-generated programs and acknowledge their use. From the educational viewpoint, students could also achieve the course learning objectives.

Source code plagiarism is an emerging issue in computer science education. As a result, a number of techniques have been proposed to handle this issue. However, comparing these techniques may be challenging, since they are evaluated with their own private dataset(s). This paper contributes in providing a public dataset for comparing these techniques. Specifically, the dataset is designed for evaluation with an Information Retrieval (IR) perspective. The dataset consists of 467 source code files, covering seven introductory programming assessment tasks. Unique to this dataset, both intention to plagiarise and advanced plagiarism attacks are considered in its construction. The dataset's characteristics were observed by comparing three IR-based detection techniques, and it is clear that most IR-based techniques are less effective than a baseline technique which relies on Running-Karp-Rabin Greedy-String-Tiling, even though some of them are far more time-efficient.