Impact of AIGC Testing on Educational Evaluation and Governance Pathways

Abstract

Currently, there is instability in AIGC detection, which is manifested in the field of education as significant differences in the AI generation rate of the same text at different times and platforms, which brings troubles to academic evaluation, graduation recognition, and so on. The root cause of this is the standard drift caused by technology iteration, the deviation of educational scenarios in training samples and the dependence of detection logic on superficial features. In this regard, it is recommended to build a “human-intelligence” collaborative governance framework: to reconstruct the evaluation system at the institutional level, and to establish a mechanism of “human-led + technology-assisted + process traceability”; to form a consensus on governance through collaboration between multiple parties; and to develop a proprietary model and standardize the standards at the technological level. The research aims to improve the reliability of testing, and to balance the academic and research standards. The study aims to improve the reliability of testing, balance academic integrity and innovation ability cultivation, and provide support for the stabilization of educational evaluation ecology in the digital era.