Responsible Institutional Analytics: Interpreting Bias with AI Support

Francielle Marques, Ariel Ortiz-Beltrán, Ishari Amarasinghe, Davinia Hernández-Leo
10/5/2026
cs.HCcs.AIcs.CL

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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.

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Cite this paper

@article{marques2026responsible,
  title  = {Responsible Institutional Analytics: Interpreting Bias with AI Support},
  author = {Francielle Marques and Ariel Ortiz-Beltrán and Ishari Amarasinghe and Davinia Hernández-Leo},
  year   = {2026},
  eprint = {2610.07205},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.07205v1}
}

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