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A Multimodal Deep Learning Approach for Legal English Learning in Intelligent Educational Systems.

Yanlin Chen^1,2, Chenjia Huang^1,3, Shumiao Gao¹

¹China Agricultural University, Beijing 100083, China.

Sensors (Basel, Switzerland)

|September 19, 2025

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A new cross-modal question-answering system enhances legal English learning by integrating visual, acoustic, and text data. This AI-powered approach significantly improves learners' understanding, expression, and satisfaction in legal contexts.

Area of Science:

Artificial Intelligence
Natural Language Processing
Educational Technology

Background:

Traditional legal English teaching struggles with multimodal inputs and complex reasoning.
Advancements in AI and sensor technologies necessitate innovative teaching approaches.
Existing systems often fail to effectively integrate diverse data types for legal education.

Purpose of the Study:

To propose a novel cross-modal legal English question-answering system.
To integrate image, text, and speech information for enhanced learning.
To improve learners' understanding and expressive abilities in legal contexts.

Main Methods:

Developed a unified vision-language-speech encoding mechanism.
Implemented dynamic attention modeling for multimodal feature alignment.

Keywords:

human-centered intelligent education multimodal semantic fusion vision–language–speech unified encoding visual and acoustic sensor integration

Related Experiment Videos

Last Updated: Jul 16, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Integrated visual and acoustic sensor inputs for question answering.

Main Results:

Achieved superior performance in question-answering accuracy (Accuracy: 0.87, Precision: 0.88, Recall: 0.85).
Demonstrated optimal multimodal matching performance (Matching Accuracy: 0.85).
User studies showed significant improvements in understanding (0.78), expression (0.75), and satisfaction (0.88).

Conclusions:

The proposed system effectively handles multimodal inputs and complex reasoning in legal English education.
It significantly outperforms traditional methods and unimodal systems in various metrics.
The system shows substantial potential for enhancing comprehensive learner capabilities and experiences.