Integrating Prescriptive Analytics, XAI, and Missing Data Handling into a Trust-centric Ensemble Model for Persuasive Interventions: A Scoping Review in Higher Education

This scoping review explores the potential of integrating prescriptive analytics, eXplainable AI, and missing data handling techniques into a trust-centric ensemble model to enhance the persuasiveness of optimised actionable insights. We analysed relevant studies that discuss prescriptive analytics, transparency and missing data handling. Our findings from a PRISMA-ScR guided study reveal limited research on prescriptive analytics, especially in higher education contexts. Trust-building and persuasive communication within educational data science is emerging alongside the established fields of eXplainable AI and missing data handling. Key themes include the role of trust in technology adoption, persuasive communication strategies in educational interventions, and the importance of data integrity in building credible models. This review underscores the need for comprehensive research to develop an integrated, trust-centric, and robust prescriptive analytics ensemble model that can generate persuasive actionable outcome for stakeholders to adopt and act upon.