TITLE:
The Future of Learning in K-12 Education: Reconstructing the School-Learner-Home Relationship through Artificial Intelligence
AUTHORS:
Edwin Ohiorenuan Imohimi, Oluwaseyi Oyetunji
KEYWORDS:
Artificial Intelligence in Education, K-12 Learning Ecosystems, Triadic Learning Model, Personalized Learning, Parental Engagement, Learning Analytics, Algorithmic Fairness
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.18 No.2,
May
27,
2026
ABSTRACT: The evolving demands of modern K-12 education necessitate a fundamental reconceptualization of instructional architecture from fragmented, institution-centric delivery models toward integrated, data-driven learning ecosystems in which schools, learners, and homes operate as a coherent unit. This paper examines the triadic relationship between the school, the learner, and the home as a structurally foundational, yet historically underutilized, configuration for sustained educational effectiveness. Drawing on Bronfenbrenner’s ecological systems theory, Vygotsky’s sociocultural framework, and constructivist principles of knowledge building, the paper argues that the persistent disconnect among these three entities constitutes a systemic design failure—one that manifests as diminished student engagement, delayed identification of learning difficulties, and suboptimal academic outcomes across K-12 contexts. To address this failure, the paper introduces and develops the concept of Artificial Intelligence as a Coordination Layer (AI-CL), a novel theoretical and architectural construct through which AI-powered systems mediate real-time information flows, adaptive feedback mechanisms, and collaborative decision-making among schools, learners, and caregivers. The AI-CL framework is distinguished from prior AI-in-education models by its explicit focus on triadic integration rather than isolated learner-system interaction. Through conceptual modeling and design-based reasoning, the paper delineates the functional components of AI-CL, examines its implications for K-12 pedagogical practice and school leadership, and critically addresses the ethical dimensions of AI deployment with minors, including data privacy under FERPA and COPPA, algorithmic bias, the digital divide, and the imperative of equitable design. The paper concludes by situating AI-CL within the broader trajectory of educational technology research and proposing a validation agenda for future empirical study.