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AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes

◐ medium · 2026-06-10k-12, adaptive-learning, personalized-learning, formative-assessment, intelligent-tutoring, edtech-platform, llm, higher-ed, generative-ai, student-experience

Misan Paul Etchie, Taiwo Olutosin — cs.CY, cs.AI, cs.HC

This paper proposes an AI-integrated LMS designed specifically for middle school instruction, addressing the gap between current LMS platforms (which function as workflow tools) and the need for real-time instructional support during the critical middle-school learning window. Key features include policy-gated AI assistance (bounded AI to avoid Over Reliance), formative feedback and hinting during practice, spaced review and adaptive practice recommendations, and teacher dashboards for misconception patterns. The system is designed with privacy-first architecture including data minimization, age-appropriate response constraints, auditable logs, and role-based access control. The proposed longitudinal study tracks students from middle school through high school into post-high school pathways, linking fine-grained learning traces to institutional outcomes. This design study is complementary to Intelligent Tutoring systems research and Personalized Learning implementations in K-12 settings.},

The emphasis on bounded AI support — rather than open-ended tutoring — distinguishes this approach from systems like Khan Academy's Khanmigo and reflects pedagogical concerns about Scaffolding versus answer-giving in K-12 AI tools.

Connected Concepts

  • Over Reliance
  • Intelligent Tutoring
  • Personalized Learning
  • Scaffolding

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    • Citation

    Etchie, M. P., & Olutosin, T. (2026). AI-Integrated Learning Management System for Middle School: A Longitudinal Study of Learning Outcomes Through High School and Beyond. arXiv:2606.07544.