Qualitative interview study reveals key motivations and verification strategies among generative AI users, highlighting internal and external mechanisms for judging content credibility.
The evolution of artificial intelligence (AI) facilitates the creation of multimodal information of mixed quality, intensifying the challenges individuals face when assessing information credibility. Through in-depth interviews with users of generative AI platforms, this study investigates the underlying motivations and multidimensional approaches people use to assess the credibility of AI-generated information. Four major motivations driving users to authenticate information are identified: expectancy violation, task features, personal involvement, and pre-existing attitudes. Users evaluate AI-generated information’s credibility using both internal (e.g. relying on AI affordances, content integrity, and subjective expertise) and external approaches (e.g. iterative interaction, cross-validation, and practical testing). Theoretical and practical implications are discussed in the context of AI-generated content assessment.
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Ou et al. (2024) studied this question.
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