Adaptive learning technologies observe learner performance, infer mastery, and dynamically tailor instruction. However, this approach can fall short when encountering a learning phenomenon that we term “deceptive overgeneralization,” where learners perform correct actions based on incomplete understanding. This phenomenon “deceives” adaptive systems into prematurely stopping necessary practice, leaving overgeneralization unaddressed. To address this, we propose a theoretical model of deceptive overgeneralization, grounded in Adaptive Control of Thought-Rational (ACT-R) and the Knowledge-Learning-Instruction (KLI) framework. This work contributes both theoretically and practically to enhancing the precision of adaptive learning, enabling more accurate mastery assessments and improved learning outcomes.