提出学生与AI协作编程中的认知素养框架，揭示多数互动缺乏深度思考。
Constructing Epistemic AI Literacy: Detecting Epistemic Aims and Processes in Student-AI Co-Programming
- 构建基于认知目标与过程的AI素养框架，分析人机协作中的思维模式。
- 78.8%互动以非掌握型目标为主，依赖外包与验证式提问等低效策略。
- 仅11.1%互动展现高阶认知参与，体现主动解释与论证能力。
认知思维在学生使用生成式人工智能（GenAI）进行编程学习中起核心作用，学习者需构建查询、评估并验证AI输出、调控解题策略。本研究提出认知型AI素养（EAIL）概念框架，将AI素养视为跨领域人机动态交互中涌现的过程性认知现象。基于AIR框架（认知目标、理想与可靠认知过程），研究考察了在GenAI支持的协同编程中认知目标与认知过程的实现方式，并探索其在交互数据中的可操作化路径。基于大规模人机协同编程对话数据，研究识别出认知目标的可观测维度（如掌握型目标）与认知过程的五类表现：外包、寻求解释、寻求验证、提示监控与认知辩护。结果表明，78.8%的学生-GenAI互动依赖非掌握型目标和较不可靠的认知策略（如外包、验证寻求），仅有11.1%的互动展现出高水平认知参与，即掌握型目标与高级认知策略（如认知辩护）相结合的可靠过程。
原文摘要 · Abstract (English)
Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies. This study introduces the conceptual framework of Epistemic AI Literacy (EAIL), reframing AI literacy as a process-oriented epistemic phenomenon that emerges through dynamic human-AI interactions across different domains. Drawing on the AIR (epistemic aims, ideals and reliable epistemic processes) framework, this study examines how epistemic aims and epistemic processes are enacted in GenAI-supported co-programming activities and explores scalable approaches for operationalizing these constructs in interaction data. Using a large dialogue dataset of human-AI co-programming, this study identifies observable dimensions of epistemic aims (i.e., mastery-oriented aims) and epistemic processes (i.e., outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification). The results reveal a prevalent lack of EAIL, with 78.8% of student-GenAI interactions relying on non-mastery-oriented aims and less reliable epistemic strategies like outsourcing and verification-seeking. Conversely, only 11.1% of interactions showed high epistemic engagement, where mastery-oriented aims were coupled with advanced epistemic strategies like epistemic justification in a more reliable epistemic process.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。