具有AI支持的自主阅读：一项为期八周的学生研究
How College Students Use AI to Navigate Course Readings: Evidence from an Eight-Week Study
AI总结：
本研究探讨了AI支持下学生自主阅读的认知过程，发现学生在阅读中表现出从理解到推理的认知发展，但受效率驱动，倾向于使用AI生成摘要来筛选阅读内容。
摘要：
大学生越来越多地使用AI聊天机器人来支持学术阅读，但缺乏对这些互动如何塑造其阅读体验和认知参与的细致理解。我们与15名本科生进行了一项为期八周的纵向研究，这些学生在课程中使用AI支持指定阅读。我们收集了838个提示，分布在239次阅读会话中，并开发了一种编码方案，将提示分为四个认知主题：解码、理解、推理和元认知。理解提示占主导地位（59.6%），推理（29.8%）、元认知（8.5%）和解码（2.1%）较少。大多数会话（72%）恰好包含三个提示，即阅读任务的最低要求。在会话中，学生表现出从理解向推理的自然认知进步，但这种进步被截断。在八周内，学生的参与模式保持稳定，但存在显著的个体差异。定性分析揭示了意图-行为的差距：学生认识到有效的提示需要努力，但很少应用这种知识，效率成为主要驱动因素。学生还根据兴趣和学术压力战略性地分配参与，表现出一种新的通过AI阅读而非使用AI的模式：使用AI生成的摘要作为主要材料来筛选哪些部分值得深入关注。我们讨论了AI阅读系统在支撑持续认知参与方面的设计启示。
英文摘要：
College students increasingly use AI chatbots to support academic reading, yet we lack granular understanding of how these interactions shape their reading experience and cognitive engagement. We conducted an eight-week longitudinal study with 15 undergraduates who used AI to support assigned readings in a course. We collected 838 prompts across 239 reading sessions and developed a coding schema categorizing prompts into four cognitive themes: Decoding, Comprehension, Reasoning, and Metacognition. Comprehension prompts dominated (59.6%), with Reasoning (29.8%), Metacognition (8.5%), and Decoding (2.1%) less frequent. Most sessions (72%) contained exactly three prompts, the required minimum of the reading assignment. Within sessions, students showed natural cognitive progression from comprehension toward reasoning, but this progression was truncated. Across eight weeks, students' engagement patterns remained stable, with substantial individual differences persisting throughout. Qualitative analysis revealed an intention-behavior gap: students recognized that effective prompting required effort but rarely applied this knowledge, with efficiency emerging as the primary driver. Students also strategically triaged their engagement based on interest and academic pressures, exhibiting a novel pattern of reading through AI rather than with it: using AI-generated summaries as primary material to filter which sections merited deeper attention. We discuss design implications for AI reading systems that scaffold sustained cognitive engagement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
