通过学生与AI的互动数据，揭示不同生成式AI素养水平的真实表现差异。
Tracing GenAI Literacy: Uncovering Student-AI Interaction Patterns in Academic Writing through Epistemic Network Analysis
- 用学习分析技术捕捉学生写摘要时与AI的交互行为
- 高素养学生多进行迭代优化和策略性提问，低素养者依赖直接指令
- 为真实评估学生AI素养提供可量化的数据支持
随着生成式AI在教育中的深入应用，培养学生的GenAI素养至关重要。然而，现有评估多依赖自我报告量表，缺乏对实际学习过程的洞察。本研究利用学习分析技术，收集了162名大学生在使用GenAI撰写摘要任务中的交互日志。通过语义网络分析（ENA），我们建模并比较了不同GenAI素养水平学生的提问策略。初步结果表明：高素养学生表现出迭代优化和策略性提问的特征，而低素养学生则更多采用直接生成指令的方式。该研究展示了如何利用过程数据刻画GenAI素养，为实现数据驱动的素养评估和实时干预提供了可能。
原文摘要 · Abstract (English)
As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical. However, current assessments largely rely on self-reported scales, lacking insights into how literacy manifests in actual learning processes. This study leverages Learning Analytics (LA) to bridge this gap. We collected interaction logs from 162 university students engaged in a GenAI-assisted abstract writing task. Using Epistemic Network Analysis (ENA), we modeled and compared the questioning strategies of students with varying GenAI literacy levels. Preliminary results reveal distinct interaction signatures: high-literacy students engage in iterative refinement and strategic questioning, while low-literacy students rely on direct generation commands. This work contributes to the workshop by demonstrating how process data can characterize GenAI literacy, paving the way for data-driven literacy assessment and real-time interventions.
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