Smart Innovation Hub: An AI-Enabled Information System for Challenge-Based Innovation and Capstone Project Matching in Higher Education

Abstract

Artificial intelligence (AI) and digital platforms are increasingly influencing how universities manage experiential learning, interdisciplinary collaboration, and innovation-oriented educational activities. Challenge-based capstone and graduation projects play an important role in this context because they connect technical learning with teamwork, stakeholder engagement, project management, and applied innovation. However, many universities still rely on fragmented and highly manual coordination processes, which can limit scalability, transparency, and effective alignment between project requirements and participant capabilities. This study presents Smart Innovation Hub, an AI-enabled information system developed to support challenge-based innovation and capstone-project coordination in higher education. The platform brings together challenge intake, participant profiling, AI-supported recommendations, mentor coordination, workflow governance, and human review within a shared educational innovation environment. The system operationalizes an Innovation Bridge ecosystem model that connects students, faculty mentors, research centers, and external partners through a data-supported coordination framework. A Design Science Research (DSR) methodology guided the development and pilot evaluation of the platform within a public university environment. The pilot evaluation relied on several evidence sources, including platform logs, coordinator records, stakeholder surveys, milestone documentation, and partner feedback collected during implementation activities. Early pilot observations suggested an approximate 60% reduction in average team-formation cycle time, together with positive stakeholder perceptions regarding workflow usability and recommendation quality. These findings should be interpreted as preliminary implementation indicators within a single-institution pilot environment. The study contributes an AI-enabled educational innovation ecosystem architecture, a hybrid semantic-structured recommendation framework for challenge-based coordination, and a structured workflow model that integrates explainability and human oversight into educational innovation management. The findings further suggest that AI-enabled information systems may improve the transparency and coordination of challenge-based innovation workflows while preserving institutional governance and human decision-making.