Speaker
Abstract section 4: Outcomes/results
The results reflect a training-data effect: news corpora that LLMs are exposed to likely emphasize factual, event-driven reporting (who, what, when, where) over the more interpretive, editorial style characteristic of campus feature writing. These mimicry rates carry implications for both AI detection and writing pedagogy. From a detection perspective, the text-type mismatch rate provides a register-level signal that complements—and may prove more robust than—surface-level AI detection tools, which are known to produce false positive rates of 15–50% and to disproportionately flag non-native English writers.
Abstract section 3: Content/method
The corpus comprised 112 texts distributed equally across four subcorpora: 28 student-produced articles and 84 AI-generated counterparts. All 112 texts were processed using the Multidimensional Analysis Tagger (MAT) version 1.3.3 for register analysis. To complement the quantitative comparisons, representative text pairs were selected from the corpus to illustrate how dimension-level differences manifest at the level of actual language use.
Abstract section 5: References
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Abstract section 1: Relevance
Since the public release of ChatGPT, the use of various generative artificial intelligence have rapidly become embedded in in students’ academic work . These generative AI tools l can generate coherent and ostensibly human-like responses on demand. These has thus led to rapid uptake among students for a wide range of writing-related tasks. This particular situation is even more apparent in higher education where multiple surveys revealed multiple cases of students passing fully AI-authored written assignments like journalistic texts as their own. Therein now lies pressing questions on authorship, authenticity, and the boundaries of human and machine-produced writing.
Abstract section 2: Contribution/research questions
Drawing on Biber’s multidimensional framework, this study compares the linguistic profiles of student-authored news and feature articles from a university student publication with AI-generated articles. Specifically, it (1) identifies key linguistic features and dimension score patterns across human and AI subcorpora, (2) examines the extent to which different AI systems approximate the register characteristics of student journalism, and (3) considers the pedagogical implications of these patterns for writing instruction and AI literacy in journalism education.
| Title | Multidimensional Analysis of Student & AI-Generated Articles |
|---|---|
| Teaching Context | College and university education |