Universities worldwide struggle with maintaining academic integrity due to the rise of advanced large language models (LLM) capable of generating flawless source code. Teachers need to be able to identify artificial intelligence (AI)-generated source code in student submissions accurately. There are already multiple different tools for detecting AI-generated content. Yet teachers are not using these tools. For this reason, this case study will focus on creating a prototype for automating the process of checking for AI-generated source code in submitted assignments in programming courses. Teachers at Linnaeus University (LNU) are using GitLab for submissions of programming assignments. The prototype in this case study will be created on GitLab and tested on submitted test assignments using the same structure utilized by teachers at LNU for their programming courses. The automated process will be accomplished using continuous integration (CI) pipelines on GitLab that will execute Python files that send and receive data to and from the AI-detection tool’s application programming interface (API). The received data will be represented on GitLab for teachers and students to see.

Abstract: This thesis investigates the capacity of individuals to detect AI-generated text, and the indicators that enable them to do so. This inquiry is situated in the broader theoretical context of digital capital, the digitization of society, deep mediatization, and AI literacy. Using a quantitative correlation approach, the study tested participants’ accuracy in detecting AI content, and shared factors between participants with high scores on this task. Participants were assessed on a number of self-reported demographic, digital capital, and digital society-based benchmarks in conjunction with AI detection accuracy. The study employed a mix of statistical methods, including logistic regression and point-biserial correlation matrices. However, only a few specific questions within the digital capital and digital society framework had a statistically significant impact on a participant being in the high-accuracy group, and these correlations were weak. Furthermore, two aspects of digital capital actually had a negative effect on the odds of scoring high on the text detection task.  The findings reveal that there is room for more research into what indicators influence human AI detection capabilities, and whether these skills are learnable or inherent to certain individuals. Moreover, the research highlights the necessity of fostering AI literacy, particularly if these capabilities improve human AI detection. While AI systems can ‘catch’ AI-generated text, their efficacy is mixed, and producers of AI text and evaluators are constantly locked in a game of cat-and-mouse, using evolving AI to recognize evolving AI. Thus, human skills are pivotal, lest we become even more dependent on technology in our deeply mediatized society.

This study investigates the growing integration of Artificial Intelligence (AI) in educational settings, specifically focusing on detecting AI-generated content in students’ English essays. As AI technologies like ChatGPT and Gemini become more prevalent, understanding their impact on education is crucial. This research aims to identify the linguistic features that lead English as a Foreign Language (EFL) teachers to suspect AI involvement in student work. By conducting semi-structured interviews with eight EFL teachers from lower upper secondary and high schools, the study examines their experiences and perspectives. Using the Technological Pedagogical Content Knowledge (TPCK) framework, the study analyzes the crossing of technology, pedagogy, and content knowledge, highlighting the opportunities and challenges AI presents in contemporary education. The findings show the dual role of AI as both a beneficial tool for improving learning and a challenge to maintaining academic integrity. Despite the limitations, such as the evolving nature of AI, the research highlights the need for teachers to balance the benefits of AI with preserving authentic student work. Future research directions include exploring more effective AI detection methods and understanding the long-term impact of AI on students’ critical thinking skills.