Learner Profiles

Priya, graduate researcher in ecology

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Priya writes analysis code daily — pandas, scikit-learn, some R — but has never used an AI tool beyond pasting error messages into a chatbot. She’s heard labmates rave about coding agents and is equal parts curious and skeptical. She wants to know what these tools actually do, whether her field-station data is safe, and how to use them without her analyses quietly going wrong.

Marcus, research software engineer

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Marcus supports a dozen labs and already uses Copilot autocomplete. He’s comfortable with git, CI, and code review, and wants to know what changes when the tool goes from suggesting lines to editing whole repos: what to put in context files, how to structure review, and what to tell the labs he supports about credentials and institutional policy.

Jen, PI returning to hands-on analysis

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Jen hasn’t written serious code in a decade but needs to evaluate what her students produce — increasingly with AI assistance. She wants enough hands-on experience to ask the right questions in lab meetings (“did you hold out a test set before or after preprocessing?”) and to set sensible lab policy about data, secrets, and review.