Artificial Intelligence tools, particularly Large Language Models (LLMs), are rapidly transforming how researchers work—from literature reviews to data analysis to line-editing. But when should you use these tools? How can you use them responsibly? And what are their limitations? This session provides early-career graduate students with a foundational understanding of AI technologies and practical guidance for incorporating them into academic research. We'll begin by demystifying the technology itself, exploring how natural language processing, machine learning, and high-performance computing converge to power today's commercial chatbots. Next, we'll take a critical look at what these tools can and cannot do, evaluating LLM performance across specific research tasks, discussing the serious issue of AI hallucinations and the imperative of fact-checking, and addressing the reproducibility challenges inherent in generative AI. Finally, we'll focus on ethical usage by establishing clear guidelines for when AI tools are appropriate (and when they're not), and reviewing emerging standards for properly citing and disclosing generative AI usage in your scholarly work. Whether you're curious about AI or already experimenting with these tools, this session will help you make informed, responsible decisions about integrating AI into your research workflow.

Experience Level: Entry-Level. No prior coding experience or use of AI required.

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Facilitated by:  Jooyeon Hahm, Head, Data Science Training & Consultation, Center for Interdisciplinary Digital Research

About Quick Bytes:

Get valuable professional development wisdom that you can apply right away! Quick Bytes sessions cover a variety of topics and include lunch. Relevant to graduate students at any stage in any degree program.

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