Real-Time Content Personalization Engine for Education Platforms Using Synthetic Tutors

With the growing level of AI involvement in individual learning, it is still challenging to adapt in real-time in cognitive and emotional conditions of learners. The current systems are based on either pre-programmed profiles of the learners or a batch update, and this does not offer flexibility and interactivity particularly in complex or interactive topics. To resolve this, we come up with the Real-Time Adaptive Synthetic Tutor Engine (RASTE), an AI-aided mechanism, which adapts the instruction dynamically to the real-time cognitive and emotional information collected by the learners. RASTE is based on a content micro-personalization engine and multi-modal pedagogical algorithms, which provide an individual needs-based explanation- or questioning-driven tutoring. RASTE improves the learning process by combining real-time interaction tracked with adaptive content delivery to promote increased engagement, retention and learning efficiency. Its architecture allows multi-agent synthetic tutors of multidisciplinary/complex subjects that allow scale, privacy-conscience, and personalized learning experiences compared to traditional adaptive systems.

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