AI 中文总结
研究学生对生成式人工智能的心智模型，通过收集64名修读技术伦理学课程学生的概念图，分析发现五类心智模型，揭示学生多为表面理解，据此可设计课程和指导方针提升学生人工智能素养。
AI 中文摘要
本文介绍了一项关于学生对生成式人工智能（GenAI）心智模型的研究。学生对GenAI的心智模型不仅影响他们对该技术能力和局限性的认知，还影响他们将其融入学术工作的方式。本研究探讨了两个问题：一是本科生对GenAI持有怎样的心智模型；二是这些心智模型中存在哪些概念知识方面——陈述性、程序性和条件性知识。通过收集64名修读技术伦理学课程学生的概念图并分析，发现了五类心智模型：基于技术过程、基于教育工具、过渡模型、后果意识模型、综合模型。陈述性知识在各图中占主导，这表明学生大多仅表面理解GenAI，对其工作原理的程序性理解和使用时机及原因的条件性知识有限。通过识别学生的心智模型，我们可以通过设计课程和指导方针来提高学生的人工智能素养，在确保负责任和符合道德使用的同时提升认知。
英文摘要
In this paper we present a study of students' mental models of generative AI (GenAI). A student's mental model of GenAI influences not only how they perceive the technology's capabilities and limitations but also how they choose to integrate it into their academic work. Whether they view it as a collaborative partner, a shortcut to complete tasks, or something in between, depends on how they conceptualize its use. This study addresses the following questions: (I) What mental models do undergraduate students hold about GenAI? and (II) What aspects of conceptual knowledge - declarative, procedural, and conditional - are present in these mental models? Sixty-four concept maps were collected from students enrolled in a course on technology ethics. Students were asked to construct concept maps representing their understanding of GenAI use. The concept maps were analyzed using a structured codebook and the analysis revealed five categories of mental models: technical process based, educational tool based, transition model, consequence aware model, integrated model. Declarative knowledge was most dominant across maps, suggesting that students largely understood GenAI primarily at a surface level - knowing its names, tools, and applications but demonstrate limited procedural understanding of how it works and limited conditional knowledge about when and why it should or should not be used. By identifying students' mental models, we can improve students' AI literacy by designing curriculum and guidelines that improve cognition while ensuring responsible and ethical use.
CommentsPaper accepted at IEEE FIE 2026
