Gabrielle O'Brien

Gabrielle O'Brien, Alexis Parker, Nasir Eisty et al.

Programming is essential to modern scientific research, yet most scientists report inadequate training for the software development their work demands. Generative AI tools capable of code generation may support scientific programmers, but user studies indicate risks of over-reliance, particularly among inexperienced users. We surveyed 868 scientists who program, examining adoption patterns, tool preferences, and factors associated with perceived productivity. Adoption is highest among students and less experienced programmers, with variation across fields. Scientific programmers overwhelmingly prefer general-purpose conversational interfaces like ChatGPT over developer-specific tools. Both inexperience and limited use of development practices (like testing, code review, and version control) are associated with greater perceived productivity -- but these factors interact, suggesting formal practices may partially compensate for inexperience. The strongest predictor of perceived productivity is the number of lines of generated code typically accepted at once. These findings suggest scientific programmers using generative AI may gauge productivity by code generation rather than validation.

Gabrielle O'Brien, Antonio Pedro Santos Alves, Sebastian Baltes et al.

Programming assistants powered by large language models (LLMs) have become widely available, with conversational assistants like ChatGPT proving particularly accessible to less experienced programmers. However, the varied capabilities of these tools across model versions and the mixed availability of extensions that enable web search, code execution, or retrieval-augmented generation create opportunities for user misconceptions about what systems can and cannot do. Such misconceptions may lead to over-reliance, unproductive practices, or insufficient quality control in LLM-assisted programming. Here, we aim to characterize misconceptions that users of conversational LLM-based assistants may have in programming contexts. Using a two-phase approach, we first brainstorm and catalog user misconceptions that may occur, and then conduct a qualitative analysis to examine whether these conceptual issues surface in naturalistic Python-programming conversations with an LLM-based chatbot drawn from an openly available dataset. Indeed, we see evidence that some users have misplaced expectations about the availability of LLM-based chatbot features like web access, code execution, or non-text output generation. We also see potential evidence for deeper conceptual issues around the scope of information required to debug, validate, and optimize programs. Our findings reinforce the need for designing LLM-based tools that more clearly communicate their programming capabilities to users.