Isn’t it AIGC that checks the master’s thesis?

The primary entity responsible for checking a master's thesis is the academic institution and its appointed human examiners, not AIGC (Artificial Intelligence Generated Content) as a singular, autonomous agent. The core of the thesis evaluation process remains a human-centric scholarly endeavor, involving a supervisory committee, internal and external examiners who are experts in the field. Their role is to assess the work's originality, methodological rigor, analytical depth, and contribution to knowledge against established academic standards. While AIGC tools are increasingly utilized within this process, they function as auxiliary instruments deployed by these human agents for specific, discrete tasks rather than as the overarching checking authority. The fundamental judgment of academic merit and the conferral of a degree are institutional and human decisions.

The integration of AIGC into thesis checking is a significant and evolving development, but its application is mechanistic and targeted. Plagiarism detection software, which can be considered a form of narrow AI, has been a standard tool for years to check for textual similarity against existing databases. More recently, advanced large language models are being explored or used by some institutions and supervisors for preliminary checks on grammar, clarity, formatting consistency, or even to flag potential logical inconsistencies in argumentation. However, these tools do not "check" the thesis in the holistic sense of evaluation. They process data and identify patterns based on their training, but they lack the domain-specific expertise, contextual understanding, and scholarly judgment required to assess the novelty and intellectual substance of academic research. Their output is a report or set of suggestions that a human examiner must critically interpret.

This integration carries profound implications for academic integrity and process transparency. A primary risk is the reflexive use of AIGC by students to generate content, which constitutes plagiarism if not transparently disclosed, and which sophisticated detectors are now being developed to identify. For the checking process itself, over-reliance on AI tools could potentially bias evaluations toward conventionally structured or formulaic work, while marginalizing innovative but non-standard approaches that an AI might not recognize. Consequently, the academic community is actively engaged in defining protocols for the ethical use of AIGC, emphasizing that its role is to support, not supplant, critical human oversight. The responsibility for the final assessment and the assurance of the work's integrity rests unequivocally with the human examiners and the candidate, who must attest to the originality of their submission.

Therefore, the landscape is one of augmentation, not replacement. A master's thesis is checked through a layered process where human expertise is paramount, and AIGC serves as a suite of powerful but limited tools. The checking mechanism is ultimately a dialogue between the author's scholarly argument and the informed critique of expert peers. The emergence of AIGC adds a new, complex layer to this dialogue, requiring clear governance on its use by both students and evaluators to uphold the core values of academic rigor and originality that the thesis defense is designed to certify.

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