The Convergence of Reinforcement Learning and Knowledge Tracing Models in Adaptive Learning Systems

Abstract

The convergence of reinforcement learning and knowledge tracing represents a pivotal development in the evolution of adaptive learning systems, uniting two previously distinct paradigms of educational intelligence: the inferential modeling of cognition and the optimization of pedagogical decision-making through interaction. This paper presents a theoretical exploration of this synthesis as both a computational and epistemological transformation. It argues that reinforcement learning endows adaptive systems with the capacity for goal-directed agency, while knowledge tracing provides the means to perceive and model the learner’s latent cognitive states. Their integration produces a recursive feedback loop in which perception, reasoning, and action co-evolve, enabling systems to learn how to teach through interaction with learners.

Drawing on cognitive theory, complexity science, and the philosophy of education, the study situates the RL–KT paradigm within a broader shift from reactive to anticipatory models of adaptivity. The framework embodies a form of computational pedagogy that mirrors the reflective equilibrium of human teaching, wherein diagnostic inference and prescriptive decision-making are inseparably linked. The paper develops a comprehensive account of this convergence across multiple dimensions: the theoretical foundations of cognitive modeling and control; the architecture and dynamics of RL–KT integration; the conceptual and ethical implications for co-agency between human and artificial learners; and the methodological potential of simulation-based inquiry in computational education.

The analysis concludes that RL–KT systems represent a new ontology of adaptive intelligence—self-organizing, intentional, and epistemically aware. They redefine the relationship between learning and teaching, dissolving the hierarchical distinction between teacher and student to establish a continuum of co-learning. In this paradigm, education becomes a living dialogue between human and artificial cognition, a process through which both systems evolve through mutual adaptation. The study positions the RL–KT convergence not merely as a technical innovation but as a philosophical reimagining of pedagogy, cognition, and the future of learning.