Challenges and Countermeasures of AIGC Content Plagiarism Checking: How to Ensure Academic Originality

With the rapid development of generative artificial intelligence technology, AIGC (Artificial Intelligence Generated Content) is increasingly widely used in academic writing. A recent study by a top-tier university found that over 35% of graduate students have attempted to use AI tools to assist in thesis writing. The “2025 Global Academic Integrity Report” shows that AIGC detection has become a new focus of academic review in universities worldwide, bringing全新的 challenges to traditional plagiarism checking mechanisms.

Analysis of Technical Difficulties in AIGC Plagiarism Checking

Current mainstream plagiarism detection systems mainly target text copying behavior, while AIGC content often has the following characteristics: semantic coherence but lack of original viewpoints, specific patterns in sentence structure, and unclear citation sources. These characteristics make it difficult for traditional plagiarism detection algorithms to accurately identify.

Specifically manifested in three dimensions of problems: first, AI-generated text reorganizes existing knowledge rather than directly copying; second, the expression methods generated by neural networks may evade conventional duplication detection; finally, some tools actively avoid plagiarism detection features. Actual test data from a journal editorial department shows that the average plagiarism rate of unmodified AIGC content is only 12-18%, far below the typical value of manual writing.

Evolution of Countermeasures by Academic Institutions

Domestic and foreign educational institutions have gradually established a multi-level AIGC identification system. From initially relying on a single plagiarism rate indicator, it has developed to incorporate the following detection methods:

• Semantic network analysis: detecting viewpoint relevance through knowledge graphs
• Writing feature recognition: analyzing indicators such as sentence complexity and vocabulary diversity
• Metadata tracing: checking document editing history and creation trajectory

It is worth noting that these methods still have a 5-15% false positive rate. A person in charge of a university graduate school revealed that they are training specialized detection models to improve AIGC recognition accuracy to over 89%.

Preventive Awareness Researchers Should Establish

Academic workers need to respond to AIGC plagiarism checking requirements from three levels:

1. Maintain critical thinking during the content creation stage and avoid over-reliance on AI tools
2. Strictly distinguish between human authors and AI-generated content when citing literature
3. Use professional systems for multi-dimensional testing before submission

Practices from a national key laboratory show that research papers using a hybrid writing mode (manual core viewpoints + AI-assisted expression) have an academic value assessment score 47% higher than pure AIGC content.

Balancing Technical Ethics and Academic Norms

During the transition period when AIGC plagiarism checking standards have not yet been unified, researchers should note that excessive prevention may inhibit the value of technology application, while complete laissez-faire will harm academic integrity. It is recommended to refer to the following principles:

• Transparently disclose the use of AI tools
• Maintain human originality in core innovation points
• Assume final responsibility for AI-generated content

The “2025 Research Ethics White Paper” proposes that the essence of AIGC plagiarism checking is to ensure the authenticity of knowledge production, rather than simply prohibiting the use of technology. This concept is being accepted by more and more academic communities.

Technical Upgrade Path for Plagiarism Checking Tools

Facing the challenges of AIGC, a new generation of plagiarism detection systems needs to break through the limitations of traditional text comparison. Specific development directions include:

• Building an AI writing feature database
• Developing dynamic semantic analysis algorithms
• Integrating multi-modal detection capabilities

Prototype systems developed by a technical team show that a hybrid architecture combining deep learning and rule engines can achieve an AIGC recognition rate of over 82%, while controlling the false positive rate within 8%.