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April 18, 20260 citationsOpen Access

A Business Intelligence Framework for AI-Powered Educational Platforms Linking Learning Analytics to Strategic Decision-Making in K-12 Schools

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MOMaduabuchukwu Augustine OnwuzurikeJEJoy Onma Enyejo

Key Points

  • To develop a comprehensive Business Intelligence framework that integrates learning analytics with strategic decision-making in K-12 education.
  • Review and synthesize contemporary research on AI-powered educational platforms.
  • Construct a multi-layered Business Intelligence framework incorporating analytics and visualization tools.
  • Examine ethical AI standards and data privacy considerations for minors.
  • Demonstrates the effectiveness of predictive modeling for identifying at-risk students.
  • Highlights the importance of prescriptive analytics in intervention planning.
  • Establishes a scalable model for both public and private educational institutions.

Abstract

Abstract: The rapid integration of artificial intelligence into K–12 educational platforms has generated unprecedented volumes of learner interaction data, yet many schools lack structured mechanisms to translate these data into actionable strategic insights. This review paper proposes a comprehensive Business Intelligence framework designed to bridge learning analytics and institutional decision-making in primary and secondary education systems. The study synthesizes contemporary research on AI-powered adaptive learning systems, predictive analytics, dashboard architectures, and data governance models to construct a multi-layered framework that aligns operational analytics with school-level strategic objectives. The proposed framework integrates data ingestion pipelines, AI-driven analytics engines, performance visualization dashboards, and executive-level reporting systems to support evidence-based planning in curriculum design, student support interventions, teacher performance evaluation, and resource allocation. Particular emphasis is placed on predictive modeling for early identification of at-risk students, prescriptive analytics for intervention planning, and KPI alignment with institutional performance benchmarks. The review further examines interoperability standards, ethical AI implementation, explainability requirements, and data privacy considerations specific to minors in educational environments. By conceptualizing educational analytics within a Business Intelligence architecture rather than isolated reporting tools, this paper advances a strategic lens through which K–12 institutions can transform raw learner data into sustainable academic performance improvements, operational efficiency gains, and equitable learning outcomes. The framework offers a scalable model suitable for public school districts, private institutions, and hybrid digital learning ecosystems seeking data-driven transformation. Keywords: Business Intelligence; Learning Analytics; AI-Powered Education; K–12 Strategic Decision-Making; Educational Data Governance. Title: A Business Intelligence Framework for AI-Powered Educational Platforms Linking Learning Analytics to Strategic Decision-Making in K-12 Schools Author: Maduabuchukwu Augustine Onwuzurike, Joy Onma Enyejo International Journal of Recent Research in Commerce Economics and Management (IJRRCEM) ISSN 2349-7807 Vol. 13, Issue 2, April 2026 - June 2026 Page No: 21-42 Paper Publications Website: www.paperpublications.org Published Date: 11-April-2026 DOI: https://doi.org/10.5281/zenodo.19510038 Paper Download Link (Source) https://www.paperpublications.org/upload/book/A%20Business%20Intelligence%20Framework%20for%20AI-Powered-11042026-1.pdf

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Cite This Study

Onwuzurike et al. (2026) studied this question.

synapsesocial.com/papers/69e3203440886becb653f4fchttps://doi.org/10.5281/zenodo.19510038
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