Generative artificial intelligence (AI) is increasingly used to support self-directed learning, yet student interaction with such systems often remains unstructured, limiting engagement in deeper cognitive processes. This study examines how instructional guidance shapes student and AI interaction in construction education. A five-step prompting framework grounded in Generative Learning Theory (GLT) is introduced to guide learner interaction during review activities. A controlled experiment compares three learning conditions: slide-based learning, unprompted AI-supported learning, and prompted AI-supported learning. Learning performance is assessed using multiple-choice and open-ended tasks, and user experience is measured using the User Experience Questionnaire (UEQ). Performance differences are concentrated on tasks requiring explanation and reasoning. The prompted condition achieves higher open-ended scores, with an improvement of approximately 2 or 3 points on a scale of 18 (p < 0.01), while no significant differences are observed in multiple-choice performance. The unprompted condition remains comparable to slide-based learning. These findings indicate that the effectiveness of AI-supported learning depends on how interaction is structured. The proposed framework provides a basis for integrating learning science principles into generative AI systems for construction education.

翻译：生成式人工智能正越来越多地用于支持自主学习，然而学生与这类系统的互动往往缺乏结构性，限制了对深层认知过程的参与。本研究考察了教学引导如何塑造建筑教育中学生与人工智能的互动。基于生成式学习理论，提出包含五个步骤的提示框架，用于指导学生在复习活动中的学习交互。通过控制实验比较三种学习条件：幻灯片学习、无提示的人工智能支持学习以及有提示的人工智能支持学习。学习绩效采用多项选择题和开放型任务进行评估，用户体验则通过用户体验问卷进行测量。绩效差异集中在需要解释和推理的任务上。有提示条件在开放型任务中得分更高，在总分18分的量表上提升约2-3分（p<0.01），而多项选择题成绩未呈现显著差异。无提示条件与幻灯片学习效果相当。研究表明，人工智能辅助学习的有效性取决于互动结构的设计。本研究所提出的框架为将学习科学原理融入建筑教育生成式人工智能系统提供了基础。