作者：赵小雨，孔桂丽，周慧，廖利婷，李秀晗

出版刊物：Smart Learning Environments

出版时间：2026年

内容摘要：

Students’ questioning competence, which is strong related to higher-order cognitive processes such as critical thinking, creativity, and problem-solving, is crucial to develop during reading. Drawing on dialogue learning theory, this study examined the effects of a human-GenAI dialogue learning mode on college students’ post reading questioning in digital reading. An instructional quasi-experiment with an in subject design was conducted among 112 college students, who completed reading tasks under both a general reading mode and a human-GenAI dialogue mode supported by ERNIE Bot. Students’ post-reading questions were analyzed in terms of quantity, cognitive level, external structure, and overall quality. Students’ output in the human-GenAI dialogue were analyzed through lag sequential analysis (LSA), complemented by representative dialogue excerpts. It was found that the application of the human-GenAI dialogue learning mode significantly improved students’ questioning behaviors and competence during reading. The lag sequence analysis of students’ strategies use in human-GenAI dialogue reveals that different strategies used in human-GenAI dialogue can affect students’ questioning performance. Students who have a higher level of self-regulation, are good at reflecting and adjusting their dialogue strategies tend to raise higher-quality questions. Overall, the findings suggest that the human-GenAI dialogue mode can serve as a scaffolded environment that supports higher-quality post-reading questioning. It may help understand how human-GenAI dialogue may support reading-related inquiry and offers implications for designing AI-supported reading activities that foster more purposeful and reflective questioning.

学生的提问能力与批判性思维、创造力和问题解决等高级认知过程密切相关，在阅读中培养这一能力至关重要。本研究借鉴对话学习理论，考察了人类—生成式AI（GenAI）对话学习模式对大学生数字阅读读后提问的影响。研究采用学科内设计的教学准实验，以112名大学生为对象，他们分别在常规阅读模式和由文心一言（ERNIE Bot）支持的人类—GenAI对话模式下完成阅读任务。研究从数量、认知水平、外部结构和整体质量方面分析学生的读后问题。通过滞后序列分析（LSA）分析学生在人类—GenAI对话中的输出，并辅以代表性对话片段。研究发现，人类—GenAI对话学习模式的应用显著提升了学生在阅读中的提问行为和提问能力。对学生人类—GenAI对话中策略使用的滞后序列分析揭示，人类—GenAI对话中使用的不同策略会影响学生的提问表现。自我调节水平较高、善于反思并调整对话策略的学生往往能提出更高质量的问题。总体而言，研究结果表明，人类—GenAI对话模式可作为一种支架式环境，支持更高质量的读后提问。这有助于理解人类—GenAI对话如何支持与阅读相关的探究，并为设计促进更有目的性和反思性提问的AI辅助阅读活动提供了启示。