Conversations With my Data: Exploring the Potential of Large Language Models in Qualitative Futures Research

The emergence of Large Language Models (LLMs), such as ChatGPT and Claude, has sparked discussions on their capacity to augment qualitative research data analysis. This study investigates using LLMs in futures research, where analysing large textual datasets is paramount. LLMs were employed experimentally to assess theme generation, patterns, and insights from infrastructure futures workshops. The study conducted a comparative analysis of Claude’s Sonnet 3.5 and ChatGPT 4o to examine workshop transcripts with and without contextual data. The data reveal that LLMs exhibit exceptional speed and efficiency but face challenges in precision, ethical implications, and the need for human supervision. This research adds to current discussions on integrating LLMs with qualitative research methods, enhancing futures analytical tools like Causal Layered Analysis, the Futures Triangle, Futures Wheel, Futures Cone, visioning, and Backcasting. The paper argues that LLMs offer a promising means of improving qualitative data analysis, maximised when used with conventional methods and human expertise.