From Detection to Prevention: Designing a Proactive Academic Integrity System for AI-Assisted Writing

Classic similarity detection techniques may not always be as useful in today's academic text processing because detection reaction time is lagging, and a high number of false positives are produced. At the same time, from this starting point, an academic integrity preemptive mechanism is designed and tested to defend against the potential problems of the writing process. Such a mode is strictly predictable and avoidable, ushering in the requirement for the breaking-through of the traditional post-accountability system. Considering the display as the foundation, the basic structure comprises three modules, that is, the deep learning algorithm that analyzes the writing style and captures any irregularities as well as BERT-based analysis of semantic reasoning and consistency to gauge the degree of AI participation, is completed. A traceable document generation chain enabled by blockchain technology highly contributes to this purpose to preserve that data in the process could never be modified and is provable. The experiment employed 500 academic texts as its sample and found that the active early warning accuracy reached only 92.3%, which was dramatically lower than the false alarm rate of conventional tools in this study (decreased by 37.8%). Besides the fact that PAIS is not just another testing tool, the study emphasizes that it is a process governance framework aimed at assisting universities in mastering the newest technology in a way that complies with ethical boundaries and transparency.