The integration of Generative AI (GenAI) into education has raised concerns about over-reliance and superficial learning, particularly in writing tasks in higher education. This study explores whether a theory-driven learning analytics dashboard (LAD) can enhance human-AI collaboration in the academic writing task by improving writing knowledge gains, fostering self-regulated learning (SRL) skills and building different human-AI dialogue characteristics. Grounded in Zimmerman's SRL framework, the LAD provided real-time feedback on learners' goal-setting, writing processes and reflection, while monitoring the quality of learner-AI interactions. A quasi-experiment was conducted involving 52 postgraduate students divided into an experimental group (EG) using the LAD to a control group (CG) without it in a human-AI collaborative writing task. Pre- and post- knowledge tests, questionnaires measuring SRL and cognitive load, and students' dialogue data with GenAI were collected and analyzed. Results showed that the EG achieved significantly higher writing knowledge gains and improved SRL skills, particularly in self-efficacy and cognitive strategies. However, the EG also reported increased test anxiety and cognitive load, possibly due to heightened metacognitive awareness. Epistemic Network Analysis revealed that the EG engaged in more reflective, evaluative interactions with GenAI, while the CG focused on more transactional and information-seeking exchanges. These findings contribute to the growing body of literature on the educational use of GenAI and highlight the importance of designing interventions that complement GenAI tools, ensuring that technology enhances rather than undermines the learning process.

翻译：生成式人工智能（GenAI）融入教育领域引发了对其可能导致过度依赖和浅层学习的担忧，这在高等教育写作任务中尤为突出。本研究探讨了理论驱动的学习分析仪表板（LAD）是否能够通过提升写作知识习得、培养自我调节学习（SRL）技能以及构建不同的人机对话特征，来增强学术写作任务中的人机协作。该LAD以齐默尔曼的SRL框架为基础，为学习者提供关于目标设定、写作过程和反思的实时反馈，同时监测学习者与AI交互的质量。研究开展了一项准实验，52名研究生被分为实验组（EG）和对照组（CG），在一项人机协作写作任务中，EG使用LAD而CG不使用。研究收集并分析了知识前测与后测、测量SRL和认知负荷的问卷数据，以及学生与GenAI的对话数据。结果显示，EG在写作知识习得方面取得了显著更高的提升，SRL技能（尤其是自我效能感和认知策略）也得到了改善。然而，EG也报告了更高的考试焦虑和认知负荷，这可能源于元认知意识的增强。认知网络分析表明，EG与GenAI进行了更多反思性、评估性的互动，而CG则侧重于更多事务性和信息寻求型的交流。这些发现丰富了关于GenAI教育应用的文献，并强调了设计能够补充GenAI工具的干预措施的重要性，以确保技术是增强而非削弱学习过程。