arXiv：cs.AI· Francielle Marques, Ariel Ortiz-Beltr\'an, Ishari Amarasinghe, Davinia Hern\'andez-Leo·· 5 小时前AI 评分32
FACTRIA：用生成式 AI 聊天机器人辅助机构分析中的偏见解读
Responsible Institutional Analytics: Interpreting Bias with AI Support
AI 导读
研究者提出 FACTRIA 框架，将机构分析（IA）中潜在的偏见因素归为分析流程、机构背景、课程特征和人口统计四类，并以其驱动一个生成式 AI 聊天机器人，在用户分析数据时提示其反思这些因素。基于四个真实 IA 案例的定性研究和转移网络分析显示，该聊天机器人促使参与者意识到此前被忽略的因素如何影响其初始解读。
正文
Abstract: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.
| Comments: | Accepted for publication in the Journal of Universal Computer Science (this http URL) |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2610.07205 [cs.HC] |
| (or arXiv:2610.07205v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07205 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Francielle Marques [view email]
[v1]
Mon, 5 Oct 2026 18:19:34 UTC (3,670 KB)
来源：arXiv：cs.AI · arxiv.org