Responsible Institutional Analytics: Interpreting Bias with AI Support
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Computer Science > Human-Computer Interaction
arXiv:2610.07205 (cs)
[Submitted on 5 Oct 2026]
Title:Responsible Institutional Analytics: Interpreting Bias with AI Support
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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)
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View a PDF of the paper titled Responsible Institutional Analytics: Interpreting Bias with AI Support, by Francielle Marques and 3 other authors
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