Michael Laudenbach
Michael Laudenbach
Assistant Professor, Humanities & Social Sciences
310 Cullimore Hall (CULM)
About Me
My research focuses on teaching writing in the disciplines, particularly in statistics & data science, using evidence-based approaches. I use computational or corpus-based linguistics to analyze large collections of both student and professional writing, transforming the results into lesson plans and learning interventions. My scholarly interests and teaching inform one another, and my theoretical grounding in rhetorical genre studies helps me to adapt these concepts to different disciplinary contexts and at various student levels. For example, I've provided communication support to undergraduate- and graduate-level engineering courses in the form of in-class lessons and writing workshops, which focus on skills that transfer to the workplace and across various contexts.
While completing my PhD at Carnegie Mellon University, I led the pilot study of an in-house digital writing tool that provided automated writing feedback to students. This study was tailored to an introductory statistics course. Collaborating with faculty in statistics & data science, I led the collection and curation of student and published writing in statistics and data science to conduct the first large-scale corpus analysis of its kind to focus on writing in those disciplines using these methods. This corpus analysis led to instructors revising their assignment prompts and grading rubrics to better match the type of writing that they ask students to produce. I’ve gained substantial experience adapting my research to the teaching of professional writing, technical academic research, presentation design, and collaborative team writing.
Recently, I’ve been working with colleagues to study variation in our statistics corpus and other corpora alongside text generated by Large Language Models (LLMs) like ChatGPT, GPT-4o, and LLaMa. Our research has identified specific stylistic differences in LLM-generated writing compared to that of humans. These results shed light on the performance of these models and challenge some of the ways LLMs are discussed, and explicitly naming these differences is increasingly important for evaluating when LLM use is appropriate and when it is not.
While completing my PhD at Carnegie Mellon University, I led the pilot study of an in-house digital writing tool that provided automated writing feedback to students. This study was tailored to an introductory statistics course. Collaborating with faculty in statistics & data science, I led the collection and curation of student and published writing in statistics and data science to conduct the first large-scale corpus analysis of its kind to focus on writing in those disciplines using these methods. This corpus analysis led to instructors revising their assignment prompts and grading rubrics to better match the type of writing that they ask students to produce. I’ve gained substantial experience adapting my research to the teaching of professional writing, technical academic research, presentation design, and collaborative team writing.
Recently, I’ve been working with colleagues to study variation in our statistics corpus and other corpora alongside text generated by Large Language Models (LLMs) like ChatGPT, GPT-4o, and LLaMa. Our research has identified specific stylistic differences in LLM-generated writing compared to that of humans. These results shed light on the performance of these models and challenge some of the ways LLMs are discussed, and explicitly naming these differences is increasingly important for evaluating when LLM use is appropriate and when it is not.
Education
M.A.; The College of New Jersey; English; 2018
B.A.; The College of New Jersey; Philosophy & English; 2017
B.A.; The College of New Jersey; Philosophy & English; 2017
2025 Spring Courses
COM 313 - TECHNICAL WRITING - HONORS
Teaching Interests
I'm interested in collaborating with instructors in math and engineering. Please contact me via email!
Journal Article
Alex Reinhart, Ben Markey, Michael Laudenbach, Kachatad Pantusen, Ron Yurko, Gordon Weinberg, David West Brown. “Do LLMs write like humans? Variation in grammatical and rhetorical styles.” National Academy of Sciences, vol. 122, no. 8.