Coding is one area in which AI assistance has been making great strides, and has been met with both enthusiasm and skepticism. From "vibe coders" to AI cynics, there is spectrum of opinions about using these tools in coding. While these tools can be used to help with review, automation, auto-completion, debugging, translation, documentation, vulnerability detection, and code generation, we recommend certain practices to ensure responsible AI-assisted programming.
In general, these tools should be used to aid and empower human coders, but the process can be challenging as you acquire the experience necessary to make the tools work for you. As with AI tools in general, LLMs used for coding are often over-confident and prone to hallucinations, and will require patience to iterate and improve the results. The work produced by AI tools, like all work, should always be reviewed, validated, and verified.
As Wills et al. note:
[T]he integration of generative AI tools such as ChatGPT holds immense promise for advancing academic research(e.g. in ecological and evolutionary fields), revolutionising coding education and practices, promoting inclusivity, and facilitating knowledge dissemination. However, researchers must navigate the ethical and methodological challenges inherent in harnessing AI technologies, prioritising transparency, validation, and critical inquiry to ensure the integrity and credibility of research initiatives. (2191)
As with any AI tool, proceed with caution and understand the the potential risks of sharing original work with AI tools and with accepting the work of GenAI without adequate review. Read Terms of Service agreements carefully and understand how your data is being used or processed.
Below are a few common tools for AI-assisted coding:
When choosing a tool, remember that these are only meant to assist you in your coding. As with other research practices, you, as the researcher and the programmer, need to be able to read the code and ensure that you understand how it works and can explain it to others. (Willison, March 19, 2025)
Consider too, that cloud-hosted LLM services can pose security and privacy concerns for researchers. In those cases, self-hosting LLMs for research purposes, could address the data privacy issues.