IMAX-AI: AN EXPLAINABLE ARTIFICIAL INTELLIGENCE DECISION SUPPORT SYSTEM FOR INNOVATION MATURITY ANALYTICS IN HIGHER EDUCATION

Luis Felipe Dias Lopes; Clarissa Stefani Teixeira

doi:10.51722/Ingi.v10.i4.6

Authors

Luis Felipe Dias Lopes Federal University of Santa Maria Author
Clarissa Stefani Teixeira Universidade Federal de Santa Catarina Author

DOI:

https://doi.org/10.51722/Ingi.v10.i4.6

Keywords:

explainable artificial intelligence, innovation maturity, decision support systems

Abstract

Innovation maturity models in higher education typically provide quantitative assessments without offering intelligent interpretive capabilities to support strategic decision-making. This study introduces IMAX-AI, an explainable artificial intelligence–driven decision-support artifact designed to extend the validated Innovation Maturity Index for Universities (IM-IU). Developed following a Design Science Research approach, IMAX-AI integrates psychometric validation, Random Forest–based feature weighting, and an explainable AI interpretive layer within a four-layer enterprise information systems architecture comprising data acquisition, quantitative maturity analytics, explainable intelligence, and governance-oriented visualization. The artifact was empirically demonstrated through its application in a public university located in Southern Brazil, illustrating its ability to identify multidimensional maturity profiles, detect structural asymmetries across institutional dimensions, and generate transparent, prioritized governance recommendations. By combining multidimensional maturity assessment with interpretable machine learning and intelligent decision support, IMAX-AI advances innovation maturity analytics beyond static benchmarking toward explainable, evidence-based governance for higher education institutions.

Author Biographies

Luis Felipe Dias Lopes, Federal University of Santa Maria

Professor Pesquisador do CNPq e FAPERGS; Professor Titular do Depto. de Ciências Administrativas (UFSM); Lider do Grupo de Pesquisa – GPCET e Vice-lider do Grupo de Pesquisa MUTARE; Pós-doutor em Engenharia e Gestão do Conhecimento (EGC/UFSC); Doutor em Engenharia de Produção (EPS/UFSC); Mestre em Engenharia de Produção (PPGEP/UFSM). Especialista em Estatística e Modelagem Quantitativa (PPGEMQ/UFSM); Licenciado em Matemática (UFN).
Clarissa Stefani Teixeira, Universidade Federal de Santa Catarina

Professora da Universidade de Santa Catarina (UFSC); Doutora em Engenharia de Produção (PPGEP/UFSC); Mestra em Distúrbios da Comunicação Humana (PPDCH/UFSM); Especialista em Atividade Fisica (UFSM); Graduada em Educação Física (UFSM).

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