A Delay-Driven Dynamic Learning Model with Cognitive Transitions and Technological Influences

Abstract This study presents an educational model that effectively responds to the dynamic and time-sensitive elements of contemporary education by integrating time delays, cognitive transitions, instructional support and technological influences. The model depicts learners' transitions through three distinct cognitive states: apathy, active learning and cognitive mastery. The analysis incorporates practical educational elements, such as delayed feedback, memory decline, distraction and the complementary influence of both human teaching and AI-powered systems. To compare the efficacy of intermittent and continuous instruction methods under various distraction and support levels, five learning scenarios were examined. The findings demonstrated that intermittent delivery of content yields the best results in an efficient classroom setting with strong teacher support. The ultimate mastery level rose from 0.57 to 0.76, demonstrating how well repetition spaced improves learning results. With a mastery level of 0.75, the fully autonomous learning system that was only assisted by AI demonstrated the highest levels of efficiency and stability. The model's high resilience to small adjustments increases its suitability for use in a variety of educational settings. This framework serves as a fundamental model for creating more efficient instructional designs, assisting in the formulation of educational policies and directing the development of adaptive education systems that are enabled by technology.