Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations

Lixiang Yan; Vanessa Echeverria; Gloria Fernandez Nieto; Yueqiao Jin; Zachari Swiecki; Linxuan Zhao; Dragan Gašević; Roberto Martinez-Maldonado

arXiv · 2311.03999

Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations

Lixiang Yan·Vanessa Echeverria·Gloria Fernandez Nieto·Yueqiao Jin·Zachari Swiecki·Linxuan Zhao·Dragan Gašević·Roberto Martinez-Maldonado

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Abstract

Generative artificial intelligence (GenAI) offers promising potential for advancing human-AI collaboration in qualitative research. However, existing works focused on conventional machine-learning and pattern-based AI systems, and little is known about how researchers interact with GenAI in qualitative research. This work delves into researchers' perceptions of their collaboration with GenAI, specifically ChatGPT. Through a user study involving ten qualitative researchers, we found ChatGPT to be a valuable collaborator for thematic analysis, enhancing coding efficiency, aiding initial data exploration, offering granular quantitative insights, and assisting comprehension for non-native speakers and non-experts. Yet, concerns about its trustworthiness and accuracy, reliability and consistency, limited contextual understanding, and broader acceptance within the research community persist. We contribute five actionable design recommendations to foster effective human-AI collaboration. These include incorporating transparent explanatory mechanisms, enhancing interface and integration capabilities, prioritising contextual understanding and customisation, embedding human-AI feedback loops and iterative functionality, and strengthening trust through validation mechanisms.

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Lixiang Yan, Vanessa Echeverria, Gloria Fernandez Nieto, Yueqiao Jin, Zachari Swiecki, Linxuan Zhao, Dragan Gašević, Roberto Martinez-Maldonado. 2023-11-07. Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations. https://arxiv.org/abs/2311.03999

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