A collaborative management model supported by artificial intelligence for institutional university accreditation projects

Abstract

University accreditation has become a strategic process for quality assurance in Higher Education Institutions, characterized by increasing regulatory, organizational, and decision-making complexity. In the Ecuadorian context, external evaluation models require cross-institutional management based on evidence and interdepartmental coordination, exposing the limitations of fragmented management approaches. In response to this scenario, the objective of this study is to propose a digital collaborative management model supported by artificial intelligence to enhance operational efficiency, information traceability, and institutional coordination in university accreditation projects. The study adopts a qualitative, applied, and propositional approach, grounded in documentary analysis of specialized scientific literature, a review of the quality assurance regulatory framework, and a conceptual modeling process. As a result, a cyclical architecture structured around five interrelated components is proposed: institutional input and project governance, intelligent core, collaborative platform, institutional output, and impact evaluation. The discussion shows that the main contribution of the model lies in its systemic and integrative approach, which goes beyond instrumental digitalization. It is concluded that the model provides a relevant conceptual framework to guide the management of university accreditation projects and opens future research avenues focused on its implementation and empirical validation.