发表机构
Rensselaer Polytechnic Institute (RPI)（伦斯勒理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
基于12门AI课程118名学生调查，识别四类GenAI用户群体，发现依赖、评估素养和课程政策显著影响学习收益与消极影响，需制定政策解决AI使用不平等问题。
AI 中文摘要
生成式人工智能（GenAI）正在改变学生的学习方式，然而课程情境、认知依赖、评估素养及早期依赖的作用仍未得到充分探讨。基于我们机构12门AI相关课程中118名学生的调查反馈，我们考察了GenAI使用及感知学习体验的差异。我们识别出四类用户群体：高使用率学生报告诸多益处，轻度使用学生报告较少依赖和较少益处，以及两个中等使用群体报告不同程度的益处。我们还发现，免费版与付费版用户、单一工具与多工具用户，以及经历不同教师政策的学生之间存在显著差异。在多变量回归模型中，学业益处与早期依赖和学术任务支持相关；积极影响与认知依赖、学术任务支持、对GenAI可靠性的信心及教师政策相关；而消极影响与早期依赖和态度变化相关。随着评估素养的提高，早期依赖与消极影响之间的关联变得更强。最后，对GenAI增强学习的感知似乎反映了认知、表现和自我效能方面的益处，而对压力和批判性思维削弱的担忧则与较低的学习收益感知相关。这些发现表明，机构需要更好的政策来解决此类不平等问题，以便使学生能够受益于日益强大的AI系统。
英文摘要
Generative artificial intelligence (GenAI) is changing how students learn, yet the roles of course context, cognitive reliance, evaluation literacy, and early reliance remain underexplored. Using survey responses from 118 students across 12 AI-related courses at our institution, we examined differences in GenAI use and perceived learning experiences. We identified four user clusters: high-use students reporting many benefits, light users reporting less reliance and fewer benefits, and two moderate-use groups reporting different levels of benefit. We also found significant differences between free- and premium-version users, single- and multiple-tool users, and students experiencing different instructor policies. In multivariable regression models, academic benefit was associated with early reliance and academic task support; positive impact was associated with cognitive reliance, academic task support, confidence in GenAI reliability, and instructor policy; and negative impact was associated with early reliance and attitudinal change. The association between early reliance and negative impact became stronger as evaluation literacy increased. Finally, perceptions of GenAI-enhanced learning appear to reflect cognitive, performance, and self-efficacy benefits, while concerns about stress and diminished critical thinking are associated with lower perceived learning benefits. These findings suggest that institutions need better policies to address such inequities so that institutions can enable students to benefit from increasingly capable AI systems.
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