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.

翻译：大学生越来越多地使用人工智能聊天机器人辅助学术阅读，然而我们对于这些互动如何塑造其阅读体验与认知投入仍缺乏细致的理解。我们开展了一项为期八周的纵向研究，15名本科生在课程中使用人工智能辅助指定阅读材料。我们收集了239个阅读会话中的838条提示，并开发了一个编码框架，将提示分为四个认知主题：解码、理解、推理和元认知。其中理解类提示占主导地位（59.6%），推理类（29.8%）、元认知类（8.5%）和解码类（2.1%）则较少出现。大多数会话（72%）恰好包含三条提示，即阅读任务规定的最低要求。在单个会话中，学生表现出从理解向推理的自然认知递进，但这种递进过程被截断。在八周的研究期间，学生的投入模式保持稳定，显著的个体差异始终存在。定性分析揭示了意图与行为之间的差距：学生认识到有效的提问需要付出努力，却很少应用这一认知，效率成为主要驱动因素。学生还根据兴趣和学业压力策略性地分配投入程度，表现出一种新型的“通过AI阅读”而非“与AI共读”的模式：将AI生成的摘要作为主要材料，用以筛选值得深入关注的章节。我们讨论了支持持续认知投入的AI阅读系统的设计启示。