IMAX-AI: An Explainable AI–Driven Decision Support System for Innovation Maturity Analytics in Higher Education

This study proposes IMAX-AI, an explainable artificial intelligence–driven decision support system designed to enhance innovation maturity analytics in higher education institutions. Positioned at the intersection of enterprise information systems, operations research, and governance analytics, the study adopts a Design Science Research approach to develop and evaluate an intelligent artifact that extends the validated Innovation Maturity Index for Universities (IM-IU). The proposed system integrates three complementary components: (i) a psychometrically validated multidimensional structure capturing institutional innovation capacity, (ii) Random Forest–based feature weighting to identify the relative importance of indicators and dimensions, and (iii) an explainable AI interpretive layer that translates quantitative outputs into transparent and actionable insights. The system architecture is structured into four interconnected layers: data acquisition and preprocessing, analytical scoring engine, explainable intelligence, and governance-oriented visualization, ensuring traceability, scalability, and integration with institutional decision environments. An empirical application involving two higher education institutions demonstrates the system’s ability to identify structural asymmetries across innovation maturity dimensions, even when institutions present similar global maturity levels. The results reveal that dimensions such as Institutional Creativity and Internationalization exert disproportionate influence on overall maturity, highlighting the limitations of aggregate indicators and reinforcing the need for analytically enriched evaluation approaches. The explainability module further enables the identification of high-impact, low-performance indicators, supporting the prioritization of strategic interventions. By embedding interpretability, feature attribution, and recommendation mechanisms into the maturity assessment process, IMAX-AI transforms static benchmarking into an analytics-driven decision support system. The study contributes to the literature by bridging maturity modeling and explainable machine learning, advancing the role of transparent AI in governance contexts. From a practical perspective, the system supports university managers in aligning innovation strategies, optimizing resource allocation, and improving institutional performance through data-driven insights. Overall, IMAX-AI represents a scalable and adaptable framework capable of supporting continuous monitoring and strategic governance of innovation in higher education, reinforcing the transition from descriptive assessment toward intelligent, explainable, and decision-oriented analytics.