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Qualitative insights from focus group discussions reveal how users interpret chatbots, indicating opacity and interpretability issues.
This article explores laypeople's folk theories about generative artificial intelligence (GAI) chatbots and the ways in which these theories are constructed. As GAI tools like ChatGPT continue to expand in reach and capabilities, understanding public perceptions of these technologies is increasingly important. Drawing on folk theory as the conceptual framework, we analyze focus group discussions to gather qualitative insights into how users rationalize the mechanisms of GAI chatbots, with particular attention to the challenges of opacity and interpretability in these technologies. Findings reveal three primary areas within users’ folk theories: knowledge sources and mechanisms, perceived characteristics, and user expectations. We find that users construct these theories by interpreting terms like “machine learning,” directly engaging with chatbots to deduce meaning from these experiences, and drawing analogies to familiar objects. Ultimately, these folk theories shape the strategies users develop for interacting with GAI chatbots.
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Z et al. (2026) studied this question.
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