负责任的高校分析：AI支持下的偏差解读
Responsible Institutional Analytics: Interpreting Bias with AI Support
- Pompeu Fabra University（庞培法布拉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对高校分析仪表盘解读易受偏差影响的问题，提出FACTRIA框架结合生成式AI聊天机器人，通过提示用户反思四类偏差因素，定性研究证明该方法能增强上下文感知的负责任解读。
AI中文摘要：
高校分析（IA）仪表盘为高等教育中的决策提供信息，然而数据限制、分析技术的约束以及缺失的上下文信息常常影响其解读。为支持更负责任的IA解读，我们引入了FACTRIA，一个将潜在偏差因素组织为四个领域的框架：分析流程、机构背景、课程层面特征和人口统计。我们将FACTRIA框架用作生成式AI聊天机器人的输入，该机器人旨在提示用户在分析IA时反思这些因素。一项基于四个真实IA案例、涉及利益相关者的定性研究以及转变网络分析表明，该聊天机器人促使参与者认识到被忽视的因素如何影响其初始解读。研究结果显示，将结构化框架与基于AI的指导相结合，可以增强对机构数据进行上下文感知、负责任解读的能力。
英文摘要：
Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and missing contextual information often affect their interpretation. To support more responsible interpretation of IA, we introduce FACTRIA, a framework that organizes potential biasing factors across four areas: the analytics pipeline, institutional context, course-level characteristics, and demographics. We used the FACTRIA framework as input to a generative-AI chatbot designed to prompt users to reflect on these factors while analyzing IA. A qualitative study with stakeholders, drawing on four authentic IA cases, and a transition network analysis showed that the chatbot prompted participants to recognize how overlooked factors influenced their initial interpretation. Findings indicated that combining a structured framework with AI-based guidance can enhance context-aware, responsible interpretation of institutional data.
