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<p>This study examines how humans interact with large language models (LLMs) in real-world, unconstrained settings, focusing on potential shifts in users’ mental models. Initially, many users approach LLMs as traditional software tools, employing structured, machine-like prompts. However, after their first interaction, a notable shift occurs, such as increased politeness, more natural language phrasing, and shorter, more contextually nuanced prompts. That is, users increasingly adopt conversational behaviors typical of human-to-human communication and, in turn, this suggests a cognitive transition in the way users perceive and engage with AI systems. Analyzing over 200,000 conversations with computational linguistics methods, we find initial indications supporting this change. These insights have implications for AI design, trust, and ethical concerns, highlighting the need for further research beyond a mostly computational perspective to strengthen our findings on how users cognitively frame their interactions with AI.</p>

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Mental model shifts in human-LLM interactions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月26日

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  • Johannes Schneider

摘要

This study examines how humans interact with large language models (LLMs) in real-world, unconstrained settings, focusing on potential shifts in users’ mental models. Initially, many users approach LLMs as traditional software tools, employing structured, machine-like prompts. However, after their first interaction, a notable shift occurs, such as increased politeness, more natural language phrasing, and shorter, more contextually nuanced prompts. That is, users increasingly adopt conversational behaviors typical of human-to-human communication and, in turn, this suggests a cognitive transition in the way users perceive and engage with AI systems. Analyzing over 200,000 conversations with computational linguistics methods, we find initial indications supporting this change. These insights have implications for AI design, trust, and ethical concerns, highlighting the need for further research beyond a mostly computational perspective to strengthen our findings on how users cognitively frame their interactions with AI.