Beyond Snapshot Learning Analytics: A Medically Informed Framework for Trajectory-Oriented Precision Learning

Artificial intelligence (AI) is increasingly embedded in educational technology systems, yet many current applications primarily optimize short-term performance metrics rather than modeling the developmental processes that shape learning over time. Drawing on learning sciences, dynamic systems theory, learning analytics, and responsible AI scholarship, this paper proposes a trajectory-oriented precision learning framework in which artificial intelligence functions as a human-centered interpretive layer for modeling state-dependent variability in learning. We introduce the Medically Informed Learning and Education (MILE) framework, an architecture that integrates contextual learner signals, longitudinal trajectory modeling, and human-in-the-loop instructional decision support. Instead of classifying learners based on static performance snapshots, the framework models learning as a dynamic developmental process and generates interpretable insights that support educator-guided adaptation. We describe the conceptual architecture of the framework, outline operational design components for educational technology systems, and illustrate potential applications across neurodiverse learners, twice-exceptional profiles, and health-related variability in learning contexts. By repositioning educational AI from static classification toward longitudinal developmental modeling, the proposed approach contributes a theoretically grounded paradigm for precision learning. The framework highlights interpretability, developmental responsiveness, and educator oversight as core design principles for next-generation educational AI systems. Implications for learning analytics, adaptive system design, and ethical governance of AI in education are discussed.