AI-Based Temporal-Causal Modeling of Educational Curiosity through Novelty and Uncertainty Dynamics

Curiosity is a foundational driver of learning, exploration, and lifelong intellectual growth. This study presents an AI-based temporal–causal model of educational curiosity that integrates psychological mechanisms with computational dynamics to explain how curiosity evolves within learning environments. The model formalizes the interactions among key factors—novelty, uncertainty, motivation, engagement, and autonomy—capturing their collective influence on learners’ adaptive behavior over time. A series of simulated learning scenarios, varying in novelty and uncertainty levels, demonstrate how contextual and personal conditions—including teacher–student relationships, socio-economic status, and environmental support—shape the temporal progression of curiosity. Mathematical and logical analyses confirm system stability, showing convergence of long-term novelty and uncertainty toward equilibrium and validating causal coherence through Temporal Trace Logic (TTL). The results reveal that curiosity thrives under moderate novelty and manageable uncertainty, reflecting adaptive equilibrium between cognitive tension and motivation. Findings provide a theoretical and computational foundation for AI-driven educational systems that dynamically sustain curiosity and engagement, contributing to inclusive and lifelong learning aligned with UN SDG 4.