Distinguishing AI-Generated and Human-Written Text Through Psycholinguistic Analysis
It addresses the need for accurate detection tools in educational settings to verify authorship and preserve academic integrity, though it builds incrementally on existing stylometric methods.
This study tackled the problem of distinguishing AI-generated from human-written text by integrating stylometric analysis with psycholinguistic theories, mapping 31 features to cognitive processes like lexical retrieval and discourse planning to highlight unique human patterns.
The increasing sophistication of AI-generated texts highlights the urgent need for accurate and transparent detection tools, especially in educational settings, where verifying authorship is essential. Existing literature has demonstrated that the application of stylometric features with machine learning classifiers can yield excellent results. Building on this foundation, this study proposes a comprehensive framework that integrates stylometric analysis with psycholinguistic theories, offering a clear and interpretable approach to distinguishing between AI-generated and human-written texts. This research specifically maps 31 distinct stylometric features to cognitive processes such as lexical retrieval, discourse planning, cognitive load management, and metacognitive self-monitoring. In doing so, it highlights the unique psycholinguistic patterns found in human writing. Through the intersection of computational linguistics and cognitive science, this framework contributes to the development of reliable tools aimed at preserving academic integrity in the era of generative AI.