Research on AIGC content detection model based on multimodal similarity for empowering sustainable development of education with digital technology

The rapid evolution of artificial intelligence-generated content (AIGC) has accelerated educational digitalization, yet its misuse—such as automated paper generation—poses unprecedented threats to academic ethics. This phenomenon not only undermines students’ critical thinking but also disrupts the fairness of educational evaluation. However, its improper application in the university context, such as students using AIGC tools to batch-generate “AI ghostwritten papers” and assignments, is increasingly eroding the foundation of academic integrity and weakening students’ critical thinking and originality. There is an urgent need for effective digital governance tools to safeguard the health and sustainability of the educational ecosystem. This study focuses on the core demands of digital technology empowering the sustainable development of education and proposes a multi-modal similarity model framework for AIGC content detection in educational scenarios. On the one hand, this model builds a three-dimensional multi-modal similarity recognition system, innovatively integrating text, formula, and chart information. On the other hand, combined with Hamming distance calculation, it achieves the deep integration of multi-source information and the identification of abnormal content through deep semantic feature extraction, structured content parsing, binary tree index structure, and local invariant feature matching technologies. Experimental verification has demonstrated the model’s accuracy and scalability. It provides digital technology support and practical paths for empowering educators and ethical governance in education to maintain a healthy academic ecosystem and educational equity, empowering learners to return to deep thinking to protect and promote the sustainable development of their core competencies such as critical thinking and innovation, and building a trustworthy, future-oriented sustainable education system.

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Publication Details

Journal
AIP Advances
Published
2025-11-01
DOI
https://doi.org/10.1063/5.0308359
Primary Topic
Academic integrity and plagiarism
Type
article
Field-Weighted Citation Impact
0.00
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article

Research on AIGC content detection model based on multimodal similarity for empowering sustainable development of education with digital technology

Dai Yuanyuan
AIP Advances
Academic integrity and plagiarism
article

Research on AIGC content detection model based on multimodal similarity for empowering sustainable development of education with digital technology

Dai Yuanyuan
article en

Abstract

The rapid evolution of artificial intelligence-generated content (AIGC) has accelerated educational digitalization, yet its misuse—such as automated paper generation—poses unprecedented threats to academic ethics. This phenomenon not only undermines students’ critical thinking but also disrupts the fairness of educational evaluation. However, its improper application in the university context, such as students using AIGC tools to batch-generate “AI ghostwritten papers” and assignments, is increasingly eroding the foundation of academic integrity and weakening students’ critical thinking and originality. There is an urgent need for effective digital governance tools to safeguard the health and sustainability of the educational ecosystem. This study focuses on the core demands of digital technology empowering the sustainable development of education and proposes a multi-modal similarity model framework for AIGC content detection in educational scenarios. On the one hand, this model builds a three-dimensional multi-modal similarity recognition system, innovatively integrating text, formula, and chart information. On the other hand, combined with Hamming distance calculation, it achieves the deep integration of multi-source information and the identification of abnormal content through deep semantic feature extraction, structured content parsing, binary tree index structure, and local invariant feature matching technologies. Experimental verification has demonstrated the model’s accuracy and scalability. It provides digital technology support and practical paths for empowering educators and ethical governance in education to maintain a healthy academic ecosystem and educational equity, empowering learners to return to deep thinking to protect and promote the sustainable development of their core competencies such as critical thinking and innovation, and building a trustworthy, future-oriented sustainable education system.

AIP AdvancesVol. 15(11)
Hohai University (CN)
Openalex Percentile: Top 66%
Academic integrity and plagiarism
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