How to responsibly deploy a predictive modelling dashboard for study advisors? A use case illustrating various stakeholder perspectives

Abstract

Most higher education institutions employ study advisors to support their students. To adequately perform their task, study advisors have access to study information about their students. Using AI techniques to analyze that information and to predict if a student might be at risk of study delay could be a valuable tool in study advisors' practice. In this paper, we present a use case of how such a tool was developed (in the form of a dashboard) and which steps and considerations played a role in the responsible deployment of the tool. Three aspects are described: first, we present the timeline of the case study and zoom in on how the macro-level of the institution (where the groundwork is laid to facilitate AI-systems in education) and the micro-level of the implementation of the system influenced each other. Second, we describe which stakeholders were involved and what their ethical considerations were concerning data management, algorithms, and pedagogy. Third, we describe the initial evaluation of the dashboard in terms of study advisors’ experiences and provide suggestions on how to stimulate the responsible and useful implementation of a predictive modelling tool.