学生提问，生成式AI助手如何回应：探索高等教育学生在学习分析反馈中的对话
What Students Ask, How a Generative AI Assistant Responds: Exploring Higher Education Students' Dialogues on Learning Analytics Feedback
AI总结：
研究探讨了学生与生成式AI助手在学习分析反馈中的对话，发现低自我调节学习能力学生寻求澄清，高能力学生则提问技术细节，AI助手在解释和个性化方面有限，但对低能力学生有帮助。
摘要：
学习分析仪表盘（LADs）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，尤其是自我调节学习（SRL）能力较低的学生往往难以参与和解读分析反馈。对话式生成式人工智能（GenAI）助手通过实时、个性化、基于对话的支持展示了潜力。进一步推进这一潜力，我们探索了学生与集成到LAD中的GenAI助手在10周学期中的真实对话。分析聚焦于不同SRL水平学生提出的问题、助手回答的相关性和质量，以及学生对助手在学习中角色的看法。发现揭示了不同的查询模式。尽管低SRL学生寻求澄清和保证，高SRL学生则查询技术方面并请求个性化策略。助手提供了清晰且可靠的解释，但在个性化、处理情绪化查询和整合多个数据点以提供定制响应方面有限。发现进一步表明，GenAI干预尤其对低SRL学生有价值，提供支持反馈参与的支架，缩小与高SRL同伴之间的差距。同时，学生的反思突显了未来系统中信任、更大适应性、情境感知和技术改进的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
