Olga Kononykhina Olga.Kononykhina@lmu.de
Version: MZES Social Science Data Lab, 2024-04-17
Ever noticed how phrases like 'delve into', "revolutionise", and 'embark on' have suddenly become really popular? We can definitely thank AI generated texts for this linguistic trend . We all played with AI tools and admired their smartness. The fastest among us have already published papers with ChatGPT or about ChatGPT. But let's face it, working with AI, doing research with AI isn't that straightforward. We can't produce good research products by asking AI to write for us, but we can certainly treat it as a mighty research assistant that will work through the volumes, leaving us to convert the volumes of information into a quality research product. In my talk, I'm going to walk you through stages of the research process and introduce you to existing AI tools that can assist you in formulating your research question, finding relevant literature, reading selected papers, analysing data, proofreading your writing, getting feedback and presenting your findings. Additionally, tools like ChatGPT can also be used to support tasks that are not directly related to research in larger projects (such as developing visual identity, creating catchy acronyms, and communication strategies). And last but not least, ChatGPT can be a motivational coach to help with writing anxiety and overcoming perfectionism in the first place. Join me to learn how we can collaborate with AI to become better at the craft of research.
📝 Slides
Olga Kononykhina is a mid-career PhD researcher at LMU Munich and the Munich Center for Machine Learning, holds degrees in Applied Mathematics and Sociology. She has 15 years of research and consulting experience in measurement systems, indicator frameworks, and data analysis in social and political contexts. She is passionate about improving data literacy and communication through interactive data platforms and storytelling. Her academic specialization focuses on enhancing occupational data quality for machine learning classification and exploring intersections of machine learning, biases, official statistics, AI governance, and privacy.