COMPARATIVE ANALYSIS OF LINGUISTICS FEATURES OF ACADEMIC TEXT AND AI-GENERATED
Keywords:
Contextual, Lexical Richness, Notions Of Syntactic Complexity, SociolinguisticAbstract
This study includes comparison of linguistic characteristics of text produced by artificial intelligence with those of human-written essays and indicates the similarities, differences, and consequences between them in addition, the acknowledgement of the language abilities of AI systems is now a key factor. This is the reason why this skill turns out to be increasingly critical owing to the fast progress of AI in natural language understanding. (NLP). Consequently, the overall purpose of this research is to provide a report on the linguistic components that are key in. the ones that humans write and texts that AI writers produce. It emphasizes the words variety, intricate grammar, unity, coherence and appropriateness in context. Comparative (analysis) is achieved through a collection of sources that are written and are authored. While other students can copy from books, academic periodicals and online resources as well as texts produced by AI. The latest NLP models. Employing computational linguistics and corpus analysis are among the set of techniques that are used for investigating and comparing linguistic characteristics. Linguistic and sociolinguistics tools in addition to qualitative assessments. The authenticity problems of the AI-generated texts in comparison with the human analogues and the consequences that follow. For instance, for scientific publications, conversational writing, and interpersonal communications besides that. The ideas on the way of changing AI in terms of linguistic characteristics. This study is a part of the bigger quest for the understanding of AI's linguistic capabilities and its use in various fields. Assisting in AI-related future development and its application.
References
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Colby, K. M., Weber, S., & Hilf, F. D. (1971). Artificial paranoia. Artificial intelligence, 2(1), 1-25.
Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023). Machine-generated text: A comprehensive survey of threat models and detection methods. IEEE Access, 11, 70977-71002.
Dehouche, N. (2021). Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). Ethics in Science and Environmental Politics, 21, 17-23.
Educause. (2019). 2019 EDUCAUSE Horizon Report. Higher Education Edition.
Fallis, D. (2021). The epistemic threat of deepfakes. Philosophy & Technology, 34(4), 623-643.
Friedman, C. (2000). A broad-coverage natural language processing system. In Proceedings of the AMIA Symposium (p. 270).
Hutson, M. (2021). Robo-writers: the rise and risks of language-generating AI.
Lyu, S. (2020, July). Deepfake detection: Current challenges and next steps. In 2020 IEEE international conference on multimedia & expo workshops (ICMEW) (pp. 1-6). IEEE.
Mirsky, Y., & Lee, W. (2021). The creation and detection of deepfakes: A survey. ACM computing surveys (CSUR), 54(1), 1-41.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35, 27730-27744.
Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and practice in technology enhanced learning, 12(1), 22.
Stern, L., & Price, R. (1958). Mad Libs [word game]. Price Stern Sloan.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
Schank, R. C., & Abelson, R. P. (1977). Scripts, plans, goals, and understanding: an inquiry into human knowledge structures.
Schulten, K. (2023, January 12). ChatGPT in the classroom: How should teachers respond? The New York Times. https://www.nytimes.com
Shearing, H., & McCallum, M. (2023, February 5). ChatGPT: What is it and how does it work? BBC News. https://www.bbc.com/news
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.
Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019). Defending against neural fake news. Advances in neural information processing systems, 32.
