Intelligent Information Systems for Educational Management: A Framework for Data-Driven Decision-Making in Nigerian Colleges of Education

The increasing volume of digital information generated by Colleges of Education creates opportunities for evidence-based educational management, yet institutional data are often distributed across separate administrative systems and are not adequately transformed into actionable intelligence. This study developed an Intelligent Information Systems (IIS) framework for data-driven decision-making in Nigerian Colleges of Education. The study adopted a Design Science Research (DSR) approach, focusing on the identification of the educational-management problem, definition of system requirements, framework design, conceptual demonstration and evaluation. The proposed framework integrates institutional data sources, data integration and quality management, an institutional data repository, Business Intelligence and analytics, Artificial Intelligence and Machine Learning, decision-support mechanisms, management dashboards, and governance, security and human oversight. The framework demonstrates how data relating to students, academic programmes, examinations, personnel, institutional resources and other administrative functions can be transformed from operational records into information and intelligent insights for management decisions. The study argues that integrating heterogeneous institutional data with appropriate analytical and intelligent technologies can strengthen evidence-based planning, monitoring, resource allocation, academic administration and institutional performance management. The framework also emphasises that Artificial Intelligence should complement rather than replace professional judgement, with privacy, security, data quality, transparency and human oversight incorporated into institutional implementation. The study contributes a Computer Science-oriented framework for addressing educational-management information challenges in Nigerian Colleges of Education and provides a conceptual foundation for subsequent prototype development and empirical evaluation.