FARG'ONA METODIKA MAKTABI-ФЕРГАНСКАЯ МЕТОДИЧЕСКАЯ ШКОЛА -FERGANA METHODICAL SCHOOL ILMIY-NAZARIY, METODIK JURNALI

Role of corpus based online tools in analysis, assessment and teaching academic writing

Основное содержимое статьи

Nasirdinova Dildoraxon Maxsudaliyevna

Аннотация

This article presents a comparative linguistic analysis of student-written and AI-generated essays composed in English based on identical prompts. Three tools were used for the analysis: LIWC, Versatile/VersaText, and Grammarly. The results revealed differences in linguistic accuracy, lexico-grammatical organization, readability, analytical nature, and cognitive characteristics. AI-generated texts demonstrated higher formal accuracy and a more pronounced analytical style, whereas human-written texts were characterized by a higher number of linguistic errors and a greater degree of authenticity. The study demonstrates that the combined use of multiple tools allows for the identification of distinct linguistic features distinguishing human and AI-generated writing and can be valuable for the analysis, assessment and teaching of academic writing.


 

Информация о статье

Как цитировать
Nasirdinova Dildoraxon Maxsudaliyevna. (2026). Role of corpus based online tools in analysis, assessment and teaching academic writing. Ферганская методическая школа, (5), 65–69. извлечено от https://ferganamethod.uz/index.php/journal/article/view/3136
Раздел
Maqolalar

Библиографические ссылки

Berber M., Sardinha T. AI-generated vs human-authored texts: A multidimensional comparison // Applied Corpus Linguistics. – 2024.

Biber D. Variation across Speech and Writing. – Cambridge: Cambridge University Press, 1988.

Fredrick D.R., Craven L. Lexical diversity, syntactic complexity, and readability: A corpus-based analysis of ChatGPT and L2 student essays // Frontiers in Education. – 2025. – Vol. 10.

Goulart L., Matte M.L., Mendoza A., Alvarado L., Veloso I. AI or student writing? Analyzing the situational and linguistic characteristics of undergraduate student writing and AI-generated assignments // Journal of Second Language Writing. – 2024.

Herbold S., Hautli-Janisz A., Heuer U., Kikteva Z., Trautsch A. A large-scale comparison of human-written versus ChatGPT-generated essays Scientific Reports. – 2023.

McEnery T., Hardie A. Corpus Linguistics: Method, Theory and Practice. – Cambridge: Cambridge University Press, 2012.

Mizumoto A., Yasuda S., Tamura Y. Identifying ChatGPT-generated texts in EFL students’ writing: Through comparative analysis of linguistic fingerprints // Applied Corpus Linguistics. – 2024.

Pennebaker J.W., Boyd R.L., Jordan K., Blackburn K. The Development and Psychometric Properties of LIWC2015. – Austin: University of Texas at Austin, 2015.

Zhao N., Lei L. Informality features in AI-generated academic writing: A corpus-based comparison between human and AI // Journal of English for Academic Purposes. – 2026.