Abstract

The integration of Human-AI collaboration in education has emerged as a transformative approach to personalize learning experiences and address diverse student needs. Traditional learning systems often fail to adapt to individual learning styles and preferences, creating challenges in maintaining engagement and improving outcomes. Deep learning and natural language processing (NLP) techniques offer innovative solutions to these challenges by analyzing learner behavior, understanding natural language inputs, and generating adaptive recommendations. This study proposes a novel framework for personalized learning systems that leverages deep learning models, such as Transformer-based architectures, and advanced NLP techniques to dynamically analyze student inputs, progress, and preferences. The framework includes human oversight to ensure ethical and pedagogically sound interventions. Using a dataset of 10,000 anonymized student interactions, the proposed system predicts learning trajectories with an accuracy of 92.5% and generates personalized recommendations with 89% relevance, significantly improving upon traditional recommendation systems by 14%. Results demonstrate the system’s ability to enhance student engagement, with a 20% increase in time-on-task and a 25% improvement in content retention scores compared to non-adaptive systems. Additionally, teacher feedback highlights a 30% reduction in workload due to automated grading and content suggestion features. The findings underline the potential of Human-AI collaboration to foster an inclusive, efficient, and engaging learning environment.