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This study reports on an evaluation of responses from 10 custom-developed chatbots to assess how chatbot answer evaluation might best be done for benchmarking and real-world environments. We asked the same 16 questions to 10 configurations of our chatbot and compared the quality of the responses provided with two key metrics: how well the chatbots’ responses scored on the Flesch-Kincaid readability test, and how well they held up to scrutiny from technical communication experts. We show how the Flesch-Kincaid readability test can be misleading and should not be considered as a comprehensive measurement when it comes to evaluating the output generated by AI models. We discuss the limitations of the Flesch-Kincaid readability test and preview our current work integrating the ISO 24495-1 Plain Language standards into the assessment of AI-driven chatbots.
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Ravichandran et al. (2026) studied this question.
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