Learning analytics dashboards (LADs) aim to support students' regulation of learning by translating complex data into feedback. Yet students, especially those with lower self-regulated learning (SRL) competence, often struggle to engage with and interpret analytics feedback. Conversational generative artificial intelligence (GenAI) assistants have shown potential to scaffold this process through real-time, personalised, dialogue-based support. Further advancing this potential, we explored authentic dialogues between students and GenAI assistant integrated into LAD during a 10-week semester. The analysis focused on questions students with different SRL levels posed, the relevance and quality of the assistant's answers, and how students perceived the assistant's role in their learning. Findings revealed distinct query patterns. While low SRL students sought clarification and reassurance, high SRL students queried technical aspects and requested personalised strategies. The assistant provided clear and reliable explanations but limited in personalisation, handling emotionally charged queries, and integrating multiple data points for tailored responses. Findings further extend that GenAI interventions can be especially valuable for low SRL students, offering scaffolding that supports engagement with feedback and narrows gaps with their higher SRL peers. At the same time, students' reflections underscored the importance of trust, need for greater adaptivity, context-awareness, and technical refinement in future systems.

翻译：学习分析仪表盘旨在通过将复杂数据转化为反馈来支持学生的学习调节。然而，学生，尤其是自我调节学习能力较低的学生，常常难以参与和解读分析反馈。对话式生成式人工智能助手已显示出通过实时、个性化、基于对话的支持来支撑这一过程的潜力。为了进一步推进这种潜力，我们探索了在一个为期10周的学期中，学生与集成在学习分析仪表盘内的生成式人工智能助手之间的真实对话。分析聚焦于不同自我调节学习水平的学生提出的问题、助手回答的相关性与质量，以及学生如何看待助手在其学习中的作用。研究结果揭示了不同的查询模式。自我调节学习能力低的学生寻求澄清和确认，而自我调节学习能力高的学生则询问技术细节并要求个性化策略。助手提供了清晰可靠的解释，但在个性化、处理情绪化查询以及整合多个数据点以提供定制化回应方面存在局限。研究结果进一步表明，生成式人工智能干预对自我调节学习能力低的学生尤其有价值，它提供了支持学生参与反馈并缩小其与高自我调节学习能力同伴之间差距的支架。同时，学生的反思强调了在未来系统中建立信任、提高适应性、情境感知能力以及技术改进的重要性。