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

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: disengaged, 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 results showed that the decline in teacher influence over time can be effectively addressed by revitalizing the teacher's role every 20 days, which achieves the best results in an ideal classroom environment. This underscores the importance of conducting periodic evaluations every 20 days to enhance sustainable educational impact. 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.