19th International Pragmatics Conference: Panel 23: AI Language and Computational Methods in Pragmatics
Short Title: IPrA

Date: 22-Jun-2025 - 27-Jun-2025
Location: The University of Queensland, Australia
Contact: Xi Chen
Contact Email: [email protected]
Meeting URL: https://ipra2025.exordo.com/login

Linguistic Field(s): Computational Linguistics; Pragmatics; Sociolinguistics

Meeting Description:

This panel brings together pragmatics research of broadly-defined AI language and research that leverages the power of AI. We welcome papers that employ statistical and computational methods as well as other quantitative approaches to analyse AI language and/or conventional pragmatics data.

This panel intends to bring together pragmatic research of ‘AI language’ and research that leverages the power of AI or computational methods to conduct pragmatics analysis. Here, AI language includes broadly AI-generated language, human-AI interactions, and human language modified by AI in any format (e.g., textual, audial). Nowadays, the application of AI permeates our linguistic experiences, from automatic sentence completion embedded in email apps to translations suggested with every click of foreign websites. It not only provides a new source of data, but also impacts or constrains human written and spoken literacy. A surge of studies has compared AI language to human language (e.g., Herbold et al., 2023; Liao et al., 2023), but rarely from a pragmatic perspective. Chen, Li and Ye (2024) was one of the first studies that confirmed GPT-3.5 had human-like pragmalinguistic and sociopragmatic performance. Still, much is unknown about the pragmatic qualities of AI language.

In the meantime, a single Large Language Model (LLM), such as GPT, is now capable of performing multiple language-processing tasks, for example, conducting POS tagging, information extraction, and sentiment analysis, rendering new opportunities to implement computational approaches in large-scale analysis of language use of human and AI (e.g. Tantucci & Wang 2022; Tay 2024). Therefore, we are also interested in the analysis of language using AI and computational methods as well as other quantitative approaches (e.g., statistical modelling, machine learning). Overall, this panel hopes to bring in innovative and/or interdisciplinary methods that can be used to analyse both conventional and AI-generated/modified language data.

References
Chen, X., Li, J., & Ye, Y. (2024). A feasibility study for the application of AI-generated conversations in pragmatic analysis. Journal of Pragmatics, 223, 14–30. https://doi.org/10.1016/j.pragma.2024.01.003
Herbold, S., Hautli-Janisz, A., Heuer, U., Kikteva, Z., & Trautsch, A. (2023). AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays (arXiv:2304.14276). arXiv. https://doi.org/10.48550/arXiv.2304.14276
Liao, W., Liu, Z., Dai, H., Xu, S., Wu, Z., Zhang, Y., Huang, X., Zhu, D., Cai, H., Liu, T., & Li, X. (2023). Differentiate ChatGPT-generated and Human-written Medical Texts (arXiv:2304.11567). arXiv. https://doi.org/10.48550/arXiv.2304.11567
Tantucci, V., & Wang, A. (2022). Resonance as an Applied Predictor of Cross-Cultural Interaction: Constructional Priming in Mandarin and American English Interaction. Applied Linguistics, 43(1), 115–146. https://doi.org/10.1093/applin/amab012
Tay, D. 2024. Data Analytics for Discourse Analysis with Python: The Case of Therapy Talk. New York: Routledge.