About

Chris M. Anson is a Distinguished University Professor and Professor of English at North Carolina State University, where he has served as the past Director of the Campus Writing and Speaking Program from 1999 to 2023. His teaching encompasses graduate and undergraduate courses in language, composition, and literacy. Prior to his tenure at NCSU, he spent fifteen years at the University of Minnesota, directing the Program in Composition and holding the title of Morse-Alumni Distinguished Professor. Anson holds a Ph.D. and a second M.A. in English with a specialization in composition studies from Indiana University, along with a B.A. in English and an M.A. in Creative Writing from Syracuse University. His research focuses on writing theory and research, response to student writing, writing across the curriculum, writing program administration, writing assessment, and digital literacies. Anson has published 19 books and over 150 journal articles and book chapters, and has been involved in numerous editorial roles and research initiatives. He has received multiple awards for teaching, research, and service, including the NC State Alumni Association Distinguished Graduate Professor Award, the Outstanding Teacher Award, and the State of Minnesota Higher Education Teaching Excellence Award. His work emphasizes the teaching and learning of writing, faculty development, and international perspectives on writing pedagogy.

Research topics

• Computer Science
• Psychology
• Artificial Intelligence
• Sociology
• Mathematics education
• Engineering management
• Engineering
• Philosophy
• History
• World Wide Web
• Linguistics
• Epistemology
• Programming language

Selected publications

• From data to treatment plan: An AI‐driven path for automated breast radiotherapy planning

  Journal of Applied Clinical Medical Physics · 2026-03-01

  articleOpen access

  Abstract Background Breast cancer is one of the most prevalent malignancies in women, with radiotherapy (RT) playing a key role in its treatment. Advances in RT techniques, such as 3D conformal radiotherapy (3D‐CRT) and intensity‐modulated radiotherapy (IMRT), have improved dose precision and reduced side effects. However, RT modality selection and treatment planning remain manual, time‐consuming, and subject to variability. Purpose This study presents and validates TARS‐B (Treatment Automation and Radiotherapy Selection for Breast Cancer), an automated framework that combines a deep learning‐based decision‐making module (DMF) for selecting the optimal RT technique and a fully automated treatment planning system (ATP) for generating deliverable plans that meet clinical quality standards and are deemed acceptable for clinical use. Materials and Methods TARS‐B functions in two stages. First, the DMF analyzes individual patient data to determine whether 3D‐CRT or IMRT is more appropriate. Second, the ATP generates the corresponding treatment plan. For 3D‐CRT, a field‐in‐field (FiF) method is used to enhance dose homogeneity and minimize hotspots. For IMRT, the DMF provides neural network‐based dose predictions, which are used to generate constraints for organs‐at‐risk (OARs). Both processes are fully scripted within the treatment planning system (TPS). The framework was tested on 60 breast cancer patients: 30 originally treated with 3D‐CRT and 30 with IMRT. Two analyses were conducted. First, the ATP's performance was evaluated by comparing automated plans with their manually generated clinical counterparts for both techniques. Second, the full TARS‐B pipeline was assessed by applying the DMF to select the RT modality and automatically generating the plan, comparing results to the original clinical plans. Dosimetric parameters, including planning target volume (PTV) coverage, OAR constraints, and low‐ and intermediate‐dose bath, were analyzed. Planning times were also compared. Results No statistically significant differences () were found between manual and automated plans in key dosimetric metrics, including PTV coverage (V95), hotspots (V105), and OAR constraints, for both 3D‐CRT and IMRT. TARS‐B confirmed the appropriateness of 3D‐CRT in all patients originally treated with it and recommended re‐planning with 3D‐CRT for 15 of 30 IMRT cases. Of these, 14 re‐plans met all criteria; one failed due to anatomical anomalies. Re‐planning led to a reduction in low‐dose bath (up to 2800 ) and intermediate‐dose bath (up to 3000 ). The reduction in intermediate‐dose bath was statistically significant (). Planning times decreased substantially: from to min for IMRT, and from to min for 3D‐CRT (). Conclusions TARS‐B effectively automates both the selection of the most appropriate RT technique and the generation of high‐quality treatment plans. This framework improves workflow efficiency, reduces planning time, and preserves dosimetric quality, highlighting its potential for clinical implementation in breast cancer RT.

  PublisherDOI

• Desafíos de la investigación transnacional sobre escritura: un enfoque heurístico / Challenges of Transnational Writing Research: A Heuristic Approach

  Revista internacional de lingüística iberoamericana · 2025-12-01

  article1st authorCorresponding

  Article Desafíos de la investigación transnacional sobre escritura: un enfoque heurístico / Challenges of Transnational Writing Research: A Heuristic Approach was published on December 1, 2025 in the journal Revista Internacional de Lingüística Iberoamericana (volume 23, issue 46).

  PublisherDOI

• Reflections on Writing and Generative AI

  Journal of Academic Writing · 2025-04-16

  articleOpen access1st authorCorresponding

  This symposium is an extension of a plenary forum on generative AI (hereafter GenAI) held at the EATAW Conference at Zurich University of Applied Sciences in Winterthur, Switzerland, in June 2023. Since the conference, AI – particularly the large language models (LLMs) shaping GenAI such as OpenAI’s ChatGPT – continue to develop rapidly with extensive integration and usage across disciplines and career sectors with educational and societal impacts. Given these developments, we recognize the central role that writing instruction has in fostering critical literacies and engaged usage and, at times, non-usage of GenAI. Just as we have adapted our teaching and learning to other technological developments, so too are we now at a time of transition and adaptation. Our initial discussion at EATAW was wide-ranging, intentionally so because (1) there is so much to explore in relation to GenAI, and (2) the EATAW membership is diverse, coming from a range of academic backgrounds. Thus in our original plenary and here in this symposium we have raised issues ranging from specific pedagogical approaches to questions of program and institutional administration, to broader public issues and conversations about the relationship of humans to machines. Here in this written symposium we each raise a different issue related to GenAI and writing with the aim to foster dialogue and discussion about GenAI in writing-related contexts.

  PublisherOA PDFDOI

• Chapter 16. The Intellectual Work of Writing Program Review

  The WAC Clearinghouse; University Press of Colorado eBooks · 2025-05-03

  book-chapterOpen access
  PublisherDOI

• Guest Editorial

  Journal of Academic Writing · 2025-04-16 · 1 citations

  editorialOpen access1st authorCorresponding

  The guest editors of this special issue of the Journal of Academic Writing present a selection of papers from the 12th Conference of the European Association for the Teaching of Academic Writing, held at Zurich University of Applied Sciences in Winterthur, Switzerland, on 5–‍7 June, 2023.

  PublisherOA PDFDOI

• Retheorizing What We See

  2025-03-12

  book-chapter1st authorCorresponding

  In this chapter, faculty members explore their teaching practices and experiences through various reflective turns, helping them to notice otherwise invisible patterns and tacitly held beliefs and opening them up for new insights and a retheorizing of what works in the classroom.

  PublisherDOI

• PO-2226 Transcriptome profiling of tumour samples could predict radioresistance in glioblastoma patients

  Radiotherapy and Oncology · 2023-05-01

  article
  PublisherDOI

• Prologue. The Multidimensional Variables of Writing Program Development and Sustainability

  The WAC Clearinghouse; University Press of Colorado eBooks · 2023-03-12

  book-chapterOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Plagiarism Detection and Intertextuality Software

  2023-01-01 · 8 citations

  book-chapterOpen access1st authorCorresponding

  Abstract Software for plagiarism detection was developed in the early 2000s when powerful search engines offered writers opportunities for unattributed copy-and-pasting from other sources. Many algorithms were developed to reveal overlaps between original and source text. Although the software was imperfect, its use has spread across higher education, precipitating intense debates about its application to the teaching of writing. Because of instructors’ fear of false accusation and the effects on students’ anxiety, many educators have eschewed plagiarism detection systems. Others, however, have adopted plagiarism detection for formative and developmental reasons, such as helping students to understand intertextuality and making referencing a manageable skill. This chapter will briefly historicize the effects of the internet on the practice of plagiarism; describe the technology behind digital programs for plagiarism detection and its functional specifications; summarize some of the research on plagiarism detection programs; describe a few of the more popular programs; and conclude with implications.

  PublisherOA PDFDOI

• Número especial: estudios de la escritura a través de las fronteras

  Literatura y lingüística · 2023-02-13

  articleOpen access1st authorCorresponding

  NA.

  PublisherDOI

Frequent coauthors

• Deanna P. Dannels

  11 shared

• Lisa Bullard

  North Carolina State University

  7 shared

• S. Peretti

  University of Geneva

  7 shared

• Robert A. Schwegler

  University of Rhode Island

  6 shared

• Ian G. Anson

  University of Maryland, Baltimore County

  5 shared

• Richard Beach

  4 shared

• Otto Kruse

  4 shared

• Simone Balocco

  Universitat de Barcelona

  3 shared

Education

• M.A., English

  Syracuse University

• M.A., English

  Indiana University

• Ph.D., English

  Indiana University

Awards & honors

• NC State Alumni Association Distinguished Graduate Professor…
• NC State Outstanding Teacher Award
• NC State College of Humanities and Social Sciences 2026 Awar…
• State of Minnesota Higher Education Teaching Excellence Awar…
• Morse-Alumni Award for Outstanding Contributions to Undergra…