Leveraging Machine Learning and NLP for Adaptive Education Systems: A Personalized Approach for Children

We present a cutting-edge AI-enabled adaptive learning system for children's education, leveraging NLP and ML approaches for a personalized, scalable experience. The system uses modern transformer-based models, such as GPT-4 and BERT, to enable precise semantic understanding and context-aware replies. This model is crucial for evaluating student inputs and scoring their semantic similarity to educational content or answer choices, enabling individualized feedback. To enhance the learning experience, the system incorporates a Reinforcement Learning (RL) layer that adjusts learning difficulty based on real-time student performance. The RL agent adjusts task complexity based on student involvement and correct/incorrect replies, ensuring a moderate learning challenge. An online adjustment method maximizes student engagement and learning gain. The method has been evaluated across multiple domains, demonstrating successful gains in accuracy, precision, memory, F1-Score, and student engagement. The performance measurements indicate that the system effectively provides context-sensitive feedback and improves learning efficiency for individual students. Age-appropriate feedback in the system fosters good learning and cognitive development in young children through clear, concise, and encouraging feedback.