Digital skill intelligent learning platform based on multi-modal interaction

By employing a multimodal input receiving unit, a cross-modal intent fusion engine, and an adaptive feedback system, the multimodal fusion and adaptation issues of existing platforms are resolved, enabling personalized learning path planning and improving learning efficiency and experience.

CN120491924BActive Publication Date: 2026-03-17CHINA NAT INST OF STANDARDIZATION
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Patent Information

Application Number
CN202510555203.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-04-29
Publication Date
2026-03-17
Estimated Expiration
2045-04-29

AI Technical Summary

Technical Problem

Existing digital skills learning platforms lack multimodal perception, cross-modal fusion, adaptive feedback, and structured knowledge representation, resulting in monotonous interaction methods, lagging feedback mechanisms, rigid learning paths, and insufficient modal conflict recognition capabilities, which increases learning difficulty and misjudgment rate, and leads to low learning efficiency.

Method used

Employing a multimodal input receiving unit, a cross-modal intent fusion engine, a multidimensional adaptive feedback system, and a digital skills knowledge graph, it achieves intelligent fusion of multimodal inputs, real-time adaptive feedback, and personalized learning path planning. Through dynamic weight allocation, conflict detection, and feedback modality combination, it provides a personalized learning experience.

Benefits of technology

It improves the efficiency and effectiveness of digital skills learning, ensures the accuracy of operational intent recognition, and enhances the immersiveness and efficiency of the learning experience.

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Abstract

This invention discloses a digital skills intelligent learning platform based on multimodal interaction, comprising a multimodal input receiving unit, a cross-modal intent fusion engine, a multidimensional adaptive feedback system, and a digital skills knowledge graph. The platform collects and preprocesses various modal signals from the user through the multimodal input receiving unit; the cross-modal intent fusion engine dynamically allocates modal weights and identifies operational intentions; the multidimensional adaptive feedback system provides corresponding visual, tactile, and auditory feedback based on the user's error rate; and the digital skills knowledge graph generates personalized learning trajectories based on user interaction data. This invention achieves accurate recognition of learning intentions under multimodal interaction, provides an immersive learning experience, and improves the efficiency and user satisfaction of digital skills learning through data-driven personalized learning path planning, effectively solving problems such as single interaction, delayed feedback, and fixed learning paths in traditional digital skills learning.
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