Should we still teach or learn coding? A postgraduate student perspective on the use of large language models for coding in ecology and evolution

Abstract

Abstract The extent to which coding skills are taught within ecology and evolution curricula remains largely unquantified. While coding, and especially R, proficiency is increasingly demanded in academic and professional contexts, many students encounter coding for the first time as postgraduates, presenting a steep learning curve alongside learning advanced statistics. With the emergence of large language models (LLMs), questions arise regarding the relevance of teaching coding when many of these tasks can now be automated. Here, we explore students' experiences with using LLMs for coding, highlighting both benefits and limitations. Through qualitative analysis of student perspectives, we identify several advantages of using LLMs for coding tasks, including enhanced search capabilities, provision of starting points and clear instructions, and troubleshooting support. However, limitations such as a lack of responsiveness to feedback and the prerequisite of extensive prior knowledge pose challenges to the effectiveness of student use of LLMs for coding at a beginner level. Concerns also arise regarding future access to LLMs, potentially exacerbating inequities in education. Despite the potential of LLMs, we argue for the continued importance of teaching coding skills alongside their integration with LLM support. Tutor‐supported learning is essential for building foundational knowledge, facilitating comprehension of LLM outputs and fostering students' confidence in their abilities. Moreover, reliance solely on LLMs risks hindering deep learning and comprehension, thereby undermining the educational process. Our experiences underscore the significance of maintaining a balanced approach, leveraging LLMs as supplementary tools rather than substitutes for coding education in ecology and evolution courses.