Stylometry Analysis of Human and Machine Text for Academic Integrity
This work proposes a unified natural language processing framework to address key challenges in academic integrity, including plagiarism, content fabrication, and authorship verification. The framework integrates four core stylometric tasks: classification of human- versus machine-generated text, distinction between single- and multi-author documents, detection of authorship changes within multi-author texts, and identification of contributing authors in collaborative writing. The study introduces and publicly releases the first academic text dataset generated using Gemini under two distinct instruction settings—standard and strict—and systematically evaluates how prompting strategies affect detection performance. Experimental results demonstrate that texts produced under strict instructions are significantly more adversarial, thereby increasing the difficulty of accurate identification. The code and dataset are made openly available, establishing a new benchmark for research on academic integrity.