Information Problem Solving (IPS) is a critical competency for academic and professional success in education, work, and life. The advent of Generative Artificial Intelligence (GenAI), particularly tools like ChatGPT, has introduced new possibilities for supporting students in complex IPS tasks. However, empirical insights into how students engage with GenAI during IPS and how these tools can be effectively leveraged for learning remain limited. Moreover, differences in background--shaped by cultural and socioeconomic factors--pose additional challenges to the equitable integration of GenAI in educational contexts. To address this gap, we present an open-source dataset collected from 279 students at a public Australian university. The dataset was generated through students' use of FLoRA, a GenAI-powered educational platform that is widely adopted in the field of learning analytics. Within FLoRA, students interacted with an embedded GenAI chatbot to gather information and synthesize it into data science project proposals. The dataset captures fine-grained, multi-dimensional records of GenAI-assisted IPS processes, including: (i) student-GenAI dialogue transcripts; (ii) writing process log traces; (iii) final project proposals with human-assigned assessment scores; (iv) two surveys assessing students demographic background and their prior knowledge and experience in data science and AI; and (v) surveys capturing students' perceptions of GenAI's effectiveness in supporting IPS and platform use experience. This dataset provides a valuable resource for advancing our understanding of GenAI's role in educational IPS and informing the design of adaptive, inclusive AI-powered learning tools.

翻译：信息问题解决（IPS）是教育、工作和生活中取得学术与职业成功的关键能力。生成式人工智能（GenAI）的出现，特别是像ChatGPT这样的工具，为支持学生完成复杂的IPS任务带来了新的可能性。然而，关于学生在IPS过程中如何与GenAI互动，以及如何有效利用这些工具促进学习的实证研究仍然有限。此外，由文化和社会经济因素塑造的背景差异，为GenAI在教育环境中的公平整合带来了额外挑战。为填补这一空白，我们提供了一个从澳大利亚一所公立大学的279名学生中收集的开源数据集。该数据集通过学生使用FLoRA（一个在学习分析领域广泛采用的GenAI驱动的教育平台）生成。在FLoRA中，学生与一个嵌入式GenAI聊天机器人互动，以收集信息并将其综合成数据科学项目提案。该数据集捕获了GenAI辅助IPS过程的细粒度、多维度记录，包括：（i）学生与GenAI的对话转录；（ii）写作过程日志追踪；（iii）带有人工评估分数的最终项目提案；（iv）两项评估学生人口统计背景及其在数据科学和AI方面先验知识与经验的调查；以及（v）调查学生对于GenAI在支持IPS方面的有效性感知及平台使用体验的问卷。该数据集为推进我们对GenAI在教育IPS中作用的理解，以及为设计自适应、包容的AI驱动学习工具提供了宝贵资源。