Meta-Learning-Based Adaptive AI Tutors for Personalized Instruction in Smart Education Platforms
Аннотация
Smart education platforms are revolutionizing modern learning by leveraging artificial intelligence to deliver personalized instruction tailored to individual students. Meta- learning, as an emerging AI paradigm, enhances this personalization by enabling rapid adaptation to diverse learner needs. However, existing intelligent tutoring systems often suffer from limitations in generalization and adaptability, requiring large amounts of labeled data and struggling to personalize instruction for new or atypical learners in real-time. To address these challenges, this paper proposes MAPLE (Meta-Adaptive Personalized Learning Engine). This novel framework integrates Model-Agnostic Meta-Learning (MAML) into AI tutors, enabling fast and efficient adaptation using limited learner data. MAPLE is designed to pre-train on a diverse range of student profiles and fine-tune rapidly to new learners with minimal interaction, thereby enhancing the precision of feedback and instructional relevance. MAPLE operates within innovative education platforms to deliver real-time personalized feedback, adaptive content sequencing, and learner-specific strategy recommendations. It dynamically models each student's learning style, pace, and cognitive gaps, making instruction more responsive and engaging. The experimental evaluation of MAPLE demonstrates superior performance in adaptability and personalization compared to traditional machine learning-based tutoring systems. Key outcomes include a significant increase in learning retention, faster convergence to learner proficiency, and higher student satisfaction scores. By minimizing the need for extensive retraining and maximizing instructional relevance, MAPLE provides a scalable and efficient solution for personalized education.