Assignments in the ChatGPT-era: Case Study on Plagiarism in Digital Systems Design Courses

Abstract

We are experiencing a prolific growth of Artificial Intelligence (AI) that is enabling its ubiquitous diffusion. As part of it, generative AI models have gained particular attention due to their promising capabilities in solving complex tasks previously associated solely to human cognitive capabilities. In this article we focus on a specific AI tool, ChatGPT, which has been developed with the vision of behaving as an educational tool tailored to everyone's learning needs. This case study analyses the capabilities of such a tool in solving a predefined set of tasks in the subject area of Digital Systems Design, with the scope of designing robust assignments for students that cannot be solved and plagiarised with this tool. The results observed across different categories of cognitive depth show that ChatGPT has extensive conceptual knowledge in the area. However, this tool has important limitations when it comes to optimisation tasks, device specific configurations and overlaying of concepts, putting an emphasis on the importance of using such aspects in the design of robust tasks.