Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent
This paper reports on recent developments of the Critical Action Learning Exchange (Carvalho et al., 2021), an international community of educators who seek to respond to social and environmental issues that affect their students. We report on an international design workshop that engaged a cohort of teachers in designing Critical Action Learning activities for their students in the Summer of 2023. Participants (n=39) completed 16 curriculum designs for grade levels from kindergarten to university, addressing a broad range of socio-environmental issues and adopting diverse approaches, such as Arts-Based Critical Action, Community Engagement, Critical Making, Games for Critical Action, and Storytelling. This paper examines our Professional Development model, together with an analysis of teacher participants’ ideas and their design products. We investigate what forms of scaffolding can facilitate the changes of practice needed for teachers to become critical action educators and support their Critical Action Learning designs.