Kai Dröge: »Why AI has a »proving the obvious« problem, and what we can do about it«

27.05.2025 - Medienberichte [Computer-Assisted Qualitative Data Analysis - A University of Surrey blog]

Guest post by Kai Dröge, Institute for Social Research Frankfurt, Germany, and University for Applied Science, Lucerne, Switzerland. Together with Colin Curtain, he develops QualCoder, an open-source QDA software with included AI support.
In February this year, Thomas Wolf, co-founder of Hugging Face, an important platform for AI research and development, talked at an event in Paris about the potential impact of AI on the advancement of science. But instead of diving into technical details, he used a personal story to illustrate his perspective on the topic. The story goes back to his time at the Massachusetts Institute of Technology (MIT). Although he had previously graduated from top French universities with excellent results, he struggled with his new role at MIT: “I was a pretty average, underwhelming, mediocre researcher” (Wolf, 2025). Moreover, the very skill that had made him a good student – the ability to quickly grasp and reproduce complex textbook knowledge – now became a major obstacle for him as a researcher: “I found it very hard to challenge the status-quo, to question what I had learned” (Wolf, 2025).