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The rapid proliferation of information and communication technologies has fundamentally reshaped the administrative landscape of educational institutions across the globe, creating both opportunities and imperatives for the modernisation of core management processes. Among these processes, the management of student admissions occupies a position of central importance, as it constitutes the primary institutional mechanism through which learners gain formal access to secondary education. In many developing countries, including Ghana, the evolution of admission systems has been marked by gradual transitions from entirely paper-based, institution-driven procedures toward partially centralised and digitally assisted models. However, despite these transitions, significant operational gaps persist at the level of individual senior high schools, gaps that continue to undermine efficiency, equity, and transparency in the admission process. This study is motivated by the recognition that existing admission systems, whether manual or centrally automated, fail to adequately address the complex, institution-specific demands of senior high school admission management in developing-country contexts. In particular, the study is concerned with the limitations of current systems in supporting real-time applicant communication, intelligent decision-making, seamless document management, and remote accessibility for students in geographically dispersed communities. To address these deficiencies, this research proposes, designs, and evaluates a cloud-based intelligent admission management system specifically tailored to the operational requirements of senior high schools.

The Senior High School (SHS) admission process in Ghana remains largely dependent on manual, paper-based workflows and fragmented digital systems that are unable to scale to the demands of a growing student population. This research proposes the design and implementation of a Cloud-Based Intelligent Admission Decision Support System (CIADSS) aimed at transforming and streamlining the SHS enrollment process. The proposed system integrates cloud computing infrastructure with machine learning algorithms to automate applicant screening, predict student placement suitability, and provide data-driven recommendations to admission officers. By leveraging cloud platforms such as Google Cloud or AWS, the system will ensure scalability, high availability, and real-time access to admission data across multiple schools and administrative offices. The intelligent layer, powered by supervised learning models, will analyse applicant academic histories, school preferences, and available placement slots to generate optimal, fair, and transparent admission decisions. A web-based dashboard will provide a user-friendly interface for school administrators, admission officers, and policy stakeholders. The research will employ a mixed-methods approach comprising system design, prototyping, and empirical evaluation through structured questionnaires and performance benchmarking. The expected outcome is a robust, scalable, and equitable admission support tool that significantly reduces administrative burden, minimises human error, and improves the overall efficiency and transparency of the SHS enrollment system in Ghana.