# Responsible Institutional Analytics: Interpreting Bias with AI Support

arXiv: https://arxiv.org/abs/2610.07205 · HTML: https://mlsanity.com/paper/2610.07205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 62 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Abstract (arXiv)

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.