AI-Driven Learning Behavior Analysis for Adaptive Content Delivery and Enhanced Productivity

The modern importance of adaptive learning platforms continues to grow because most platforms currently fail to achieve both dynamic behavior analysis and real-time personalization. An artificial intelligence system that evaluates learner conduct through a two-part approach where reinforcement learning works with decision tree classifier methods. The system tracks live student interactions to modify information distribution according to the current patterns of learner participation. The analysis relied on behavioral data from OpenEd, which included a diverse set of interactions for both training and assessment purposes. The use of the proposed model led to accuracy enhancements, reaching 96.3%, along with improvements in content relevance to 89.5% and increased learner retention to 41.7%, surpassing the existing systems from 2022 to 2024. The implemented TensorFlow and Python for its simulation runs through a cloud-based education simulation environment. The model demonstrates better delivery adaptability and learner involvement than basic regulationbased systems. It demonstrates that educational platforms containing AI behavior modeling show the potential to upgrade learning effectiveness together with real-time productivity improvements in instructional settings.