Artificial Intelligence to Enhance Qualitative Research: Methodological Reflections on a Pilot Study

Qualitative analysis is essential in research across diverse fields, offering in-depth insights that often cannot be captured through quantitative methods. However, managing large volumes of qualitative data presents challenges, including its labour intensive nature and the potential for interpretive biases. In this study, we introduce and show a methodology step by step that integrates artificial intelligence (AI) in the analysis of qualitative data, with a focus on textual responses extracted from survey questions. Specifically, our approach employs AI techniques, utilizing Word2Vec for word embedding extraction and K-Means clustering to streamline the analysis of qualitative textual data, while ultimately integrating the researcher’s interpretation of the identified clusters to improve the relevance of the analysis. Moreover, the present article discusses the relevance and significance of this approach as well as its ethical and methodological challenges by means of an empirical illustration taken from a study on teachers’ sensemaking regarding a range of different educational activities.