Intelligent educational systems based on adaptive learning algorithms and multimodal behavior modeling.

ABSTRACT: With the rapid advancement of artificial intelligence, the demand for personalized and adaptive learning has driven the development of intelligent educational systems. This article proposes a novel adaptive learning-driven architecture that combines multimodal behavioral modeling and personalized educational resource recommendation. Specifically, we introduce a multimodal fusion (MMF) algorithm to extract and integrate heterogeneous learning behavior data-including text, images, and interaction logs-via stacked denoising autoencoders and Restricted Boltzmann Machines. We further design an adaptive learning (AL) module that constructs a student-resource interaction graph and dynamically recommends learning materials using a graph-enhanced contrastive learning strategy and a dual-MLP-

SUBMITTER: Li Y 

PROVIDER: S-EPMC12453766 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature