Authors : Alain Kuyunsa Mayu; Hervé Kinkete Mfumabi; Pontien Katukumbanyi Katukumbanyi; Bruno Luwa Muanda

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/y6j9ycsv

Scribd : https://tinyurl.com/5dskf2fu

DOI : https://doi.org/10.38124/ijisrt/26apr651

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Education systems face a structural paradox: increasing demands for performance coexist with inefficient use of educational time, which remains largely treated as a fixed constraint. Despite advances in algorithmic governance and artificial intelligence in education, existing research fails to conceptualize time as a governable and optimizable variable within adaptive learning systems. This study develops a theoretical framework for the algorithmic governance of educational time and introduces the MAES-AI model (Model of Adaptive Educational Systems driven by Artificial Intelligence). Conceptualized as a closed-loop adaptive governance architecture, the model integrates diagnosis, decision-making, execution, validation, feedback, and dynamic adjustment within a recursive regulatory cycle aligning learner states, pedagogical actions, and temporal efficiency. Using a Design Science Research approach, the study formalizes the model as a theoretical artifact in which educational time is explicitly embedded as an endogenous variable. The framework further connects micro-level learning processes, meso-level institutional mechanisms, and macro-level policy dynamics. The study contributes by extending algorithmic governance to temporal optimization, reframing AI in education as a governance infrastructure, and demonstrating that learning outcomes depend on the efficiency of time allocation. It proposes a paradigm in which education systems become algorithmically governed, adaptive, and temporally optimized public infrastructures.

Keywords : Algorithmic Governance; Educational Time Optimization; Artificial Intelligence in Education; Adaptive Learning Systems; Digital Government; Human Capital; Design Science Research.

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