Benkhelifa, Fatma; Lakhani, Farha; Jendoubi, Takoua; Paltalidis, Nickos; Wijeratne, Vindya; (2025) Exploring the Use of Genai Code Assistants for Engineering Students in Transnational Education Programmes: A Pilot Study. In: 2025 IEEE Global Engineering Education Conference (EDUCON). (pp. pp. 1-7). IEEE: London, UK.

Preview

Text Jendoubi_Final Manuscript_1571108448.pdf Download (314kB) | Preview

Abstract

The effective use of Generative Artificial Intelligence (GenAI) in education is rapidly increasing at all levels and educators are exploring this technology to make the best use of it. This paper proposes exploiting GenAI to help engineering students perform their laboratory tasks effectively. In Transnational Education (TNE) programmes based on block teaching, students may face uneven intensive study load with nonconsistent face-to-face student-teacher contact throughout the term. There is a post-COVID impact even after lifting restrictions and bringing a change in students' behaviour of more reliance on online resources rather than in-class lectures. To address these concerns, this work aims to develop a GenAI-based tool that leverages student reliance on self-study while filling the gap between teacher-student interaction during non-teaching weeks. The AI mentor will be informed/trained with all the lab-related material for the module and is expected to support students during the lab sessions. In this paper, the authors present a pilot study and engage third-year engineering students to evaluate their willingness to use existing GenAI based code assistants in solving lab tasks that involve programming exercises. Results indicate students use GenAI tools either occasionally or frequently, to assist with programming-related assignments. When applied to lab tasks, majority of the students find GenAI code assistant quite helpful for understanding concepts and solving the programming tasks. Additionally, students utilize these tools for diverse purposes, including writing code, solving errors, and answering questions. Findings reveal high enthusiasm among students for incorporating GenAI based code assistants into labs. In the future, this work aims to do refinements in further studies and engage second-year engineering students as we believe engaging them will provide a broader perspective on the adoption and use of these technologies. As engineering graduates are generally expected to swiftly adapt to new technologies and digital environments, developing this ability pre-graduation is crucial for their skillset and their professional readiness.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item