Design and Implementation of a Personalized Learning System Based on Multimodal Portraits

The synergistic integration of Artificial Intelligence (AI) and Multimodal Learning Analytics (MMLA) has catalysed a paradigm shift in educational technology, moving from static instruction to dynamic, adaptive environments. However, existing systems often struggle to capture the holistic cognitive and behavioural state of learners due to reliance on unimodal data sources. To address this limitation, this paper introduces a novel personalized learning framework centered on advanced Multimodal Learner Portraits. These portraits' functions enable a granular understanding of learners' intent and preferences. The paper elaborates on the system's architecture, which incorporates a multi-stage data and leverages generative AI to enable real-time adaptive scaffolding. Unlike traditional recommendation engines, our approach leverages Large Language Models (LLMs) to dynamically generate tailored content and feedback loops based on the evolving state of the learner portrait. The study specifically focusing on how deep learning models process cross-modal interactions to predict learning trajectories and optimize content delivery. To validate the system's efficacy, extensive empirical evaluations were conducted using large-scale educational datasets. Comparative analysis reveals that this multimodal, personalized approach yields statistically significant improvements over traditional one-size-fits-all methodologies, particularly in metrics of knowledge retention, cognitive engagement, and satisfaction.