Exploring Data Science Students’ Engagement, Usage Patterns, and Perceptions of Large Language Models in Programming

Large Language Models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, paving the way for new applications previously unhandled. The use of LLMs is increasing, especially among students. This study aimed to understand how students perceive and use these technologies for programming tasks. We surveyed students and recent graduates of a Master's degree in Data Science for Health regarding their programming assignments. Among respondents (n=77), 84.4% (n=65) reported using LLMs for tasks such as debugging, generating code, understanding error messages, optimizing code and providing detailed explanations. Of these users, 55.4% (n=36) reported that LLM usage has become a daily habit, while 46.1% (n=30) noted a growing trend in their usage of LLMs. Furthermore, 87.7% (n=57) engaged in monitoring to keep up to date with the latest developments. LLMs are considered reliable by the majority of participants, however most of them still carried out verifications on their answers. Although 81.5% (n=53) of LLM users were satisfied with the tools, citing their speed, ease of use, and debugging potential, concerns about tool dependency, data confidentiality, and the precision of references were also raised. The results highlighted the uptake of these technologies by students, indicating that the integration of LLMs into educational settings is essential to promote best practices while maintaining a focus on the importance of fundamental skills such as problem-solving and critical thinking, which are indispensable in professional life. Therefore, educators are encouraged to adapt their teaching methods and assessment strategies accordingly.