[
    {
        "id": "osp-15775",
        "type": "article-journal",
        "title": "Responsible Institutional Analytics: Interpreting Bias with AI Support",
        "author": [
            {
                "family": "Marques",
                "given": "Francielle"
            },
            {
                "family": "Ortiz-Beltrán",
                "given": "Ariel"
            },
            {
                "family": "Amarasinghe",
                "given": "Ishari"
            },
            {
                "family": "Hernández-Leo",
                "given": "Davinia"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/responsible-institutional-analytics-interpreting-bias-with-ai-support",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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."
    }
]