AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning

Abstract

Teaching and learning in higher education require adaptation following students’ inevitable use of AI chatbots. This study contributes to the empirical literature on students’ use of AI chatbots and how they influence learning. The aim of this study is to identify how to adapt programming education in higher engineering education. A mixed-methods case study was conducted of a scientific computing course in a Mechanical Engineering Master’s program at a University of Technology in [blinded for review]. Data consisted of 29 student questionnaires, a semi-structured group interview with three students, a semi-structured interview with the teacher, and 29 students’ grades. Results show that students used ChatGPT for error checking and debugging of code, increasing conceptual understanding, generating, and optimizing solution code, explaining code, and solving mathematical problems. While students reported advantages of using ChatGPT, the teacher expressed concerns over declining code quality and student learning. Furthermore, both students and teacher perceived a negative influence from ChatGPT usage on pair programming, and consequently on student collaboration. The findings suggest that learning objectives should be formulated in more detail, to highlight essential programming skills, and be expanded to include the use of AI tools. Complex programming assignments remain appropriate in programming education, but pair programming as a didactic approach should be reconsidered in light of the growing use of AI Chatbots.

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