This study explored the application of artificial intelligence (AI) tools to qualitative data analysis, specifically in the context of industrial design education, and compared an AI approach with manual coding analysis (human approach). Interview transcripts from 17 industrial design educators about teaching challenges during the COVID-19 pandemic and with Generation Z students were analyzed. The human approach was based on a grounded theory method using ATLAS.ti software, while the AI approach relied on Google’s NotebookLM, which generates content based on user-provided data, to identify themes and gain insights. The findings indicate that, while both approaches can identify similar key themes, the AI approach offers significant time savings and provides a more objective analysis, free from researcher bias. However, the AI approach lacks transparency in its process and may produce divergent results upon repeated queries, unlike the convergent nature of the human approach. A hybrid approach that combines the strengths of both methods can improve the efficiency, reliability, and validity of qualitative data analysis. This study contributes to the discussion of using AI as a supplementary tool in qualitative research in the field of design education.