Greg Stanton, librarian and head of the libraries at the University of Virginia, has conducted a comprehensive analysis of the types of AI responses and how users interact with these responses. With the increasing use of AI in internet searches, users need a deeper understanding of the types of responses they receive. Stanton particularly notes that each type of response requires different judgments from the user.
Classification of AI Response Types
Stanton categorizes AI responses into four general types: factual, interpretive, structural, and strategic. Factual responses answer specific and verifiable questions, such as the founding date of the University of Virginia or the chemical symbol for gold. In this type of response, the user must verify the credibility of the information by referring to reliable sources.
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Interpretive responses are based on evidence but do not arrive at a uniform conclusion. For example, regarding how much screen time is appropriate for teenagers, responses may vary depending on the quality and context of screen use. Here, to assess the accuracy of the response, the user must pay attention to different pieces of evidence and interpretations.
Evaluating Structural and Strategic Responses
Structural responses are typically created for specific requests such as writing a letter or educational planning and therefore cannot have a correct and precise answer. Users must consider the purpose, audience, and tone of the response to have a more accurate evaluation.
Strategic responses address questions related to decision-making. For example, if a user asks whether they should take aspirin daily, the response should include relevant information and risks. In this case, the user must pay attention to their personal details and consult a doctor. Stanton emphasizes that ultimately, the final judgment should rest with the user, as they are the ones who must live with the consequences of their decisions.
Finally, Stanton advises users to be more careful when receiving AI responses and to consider the type of cognitive work that the AI agent has performed in generating the response. In this process, evaluating whether the interpretation provided is convincing or whether the advice is suitable for individual circumstances is of great importance.
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