My Setup
I run a machine learning lab with 8 PhD students. Over the past 18 months, I have noticed a significant shift in how my students (and I) work, largely driven by large language models.
Things that have genuinely improved:
- Literature search: I use LLMs to get a quick conceptual map before diving into papers
- Code debugging: GitHub Copilot has reduced boilerplate time by ~30%
- Writing: Grammar and clarity checks are now instant
Things I am cautious about:
- Students are sometimes getting confidently wrong answers about recent results
- Risk of AI-generated text without proper attribution
- I worry that relying on AI for literature summarization may weaken reading comprehension skills
The question: How are others balancing the productivity benefits against these risks? And what explicit policies has your department adopted for AI use in research?
