Linguistic Approaches to Human and AI Communication: Meaning, Interaction and Interpretation

Dear Colleagues,

The rapid advancement of large language models (LLMs) and other generative artificial intelligence (GenAI) technologies has transformed the ways in which language is produced, interpreted, and exchanged across academic, professional, educational, and everyday contexts. AI systems are now used not only to generate written texts but also to engage in spoken interaction, facilitate multilingual communication, support translation, and participate in collaborative human–AI communication. Recent evidence demonstrates the widespread adoption of AI-assisted language production across diverse domains (Herbold et al., 2023; Khalifa & Albadawy, 2024; Kobak et al., 2025). At the same time, studies consistently show that human evaluators often struggle to distinguish AI-generated communication from human communication, highlighting both the sophistication of contemporary language models and the need for systematic linguistic investigation (Casal & Kessler, 2023; Gao et al., 2023).

These developments have stimulated growing linguistic interest in examining communication involving humans and AI. Recent studies have investigated similarities and differences between human- and AI-generated communication across multiple levels of analysis, including lexical and syntactic complexity, discourse organization, rhetorical structure, stance, engagement, metadiscourse, and genre conventions (Alghazo et al., 2025; El-Dakhs et al., 2026; Georgiou, 2025; Wilson & Rose, 2025; Zhang & Zhang, 2025a, 2025b). Collectively, this growing body of research demonstrates that meaningful insights arise from integrating multiple linguistic perspectives to understand how communication constructs meaning, organizes interaction, fulfills communicative purposes, and adapts to different audiences, contexts, and modalities. As scholarship on AI communication continues to expand across diverse theoretical traditions, there is an increasing need to bring these perspectives together and foster dialogue across linguistic subdisciplines.

This Special Issue aims to bring together innovative research that advances the linguistic understanding of communication generated by AI, or co-constructed through human–AI interaction. We welcome theoretically informed and empirically grounded studies investigating spoken, written, multimodal, and AI-mediated communication using diverse linguistic frameworks and methodologies. Rather than privileging any single analytical tradition, this Special Issue seeks to promote dialogue among different branches of linguistics and to explore how established and emerging linguistic approaches can deepen our understanding of meaning, interaction, and interpretation in an era of increasingly intelligent communicative technologies.

Topics include, but are not restricted to:

• Linguistic characteristics of human, AI-generated, and human–AI communication;
• Spoken interaction with conversational AI and voice-based systems;
• Semantics, pragmatics, and discourse analysis of AI communication;
• Conversation analysis and interactional organization in human–AI dialogue;
• Corpus-based and computational linguistic investigations of AI-generated language;
• Genre analysis and rhetorical organization across communicative contexts;
• Stance, engagement, metadiscourse, evaluation, and interpersonal meaning;
• Lexical, morphological, syntactic, and stylistic variation in AI-generated language;
• Cross-linguistic, multilingual, and intercultural perspectives on AI communication;
• AI-mediated professional, educational, institutional, and public communication;
• Cognitive, functional, sociolinguistic, and critical approaches to human and AI communication;
• Methodological innovations for investigating language produced by humans and AI.

We request that, prior to submitting a manuscript, interested authors initially submit a proposed title and an abstract of 400–600 words summarizing their intended contribution. Please send it to the Guest Editors (ddakhs@psu.edu.sa, dogan.yuksel@open.ac.uk + w2113@shisu.edu.cn) or to the Languages editorial office (languages@mdpi.com). Abstracts will be reviewed by the Guest Editors for the purposes of ensuring proper fit within the scope of the Special Issue. Full manuscripts will undergo a double-blind peer review.

Tentative completion schedule:

Abstract Submission Deadline: 15 September 2026 
Notification of Abstract Acceptance: 15 October 2026 
Full Manuscript Deadline: 15 March 2027 

References

1. Alghazo, S., Rabab’ah, G., El-Dakhs, D. A. S., & Mustafa, A. (2025). Engagement strategies in human-written and AI-generated academic essays: A corpus-based study. Ampersand, 15, 100237. https://doi.org/10.1016/j.amper.2025.100237
2. Casal, J. E., & Kessler, M. (2023). Can linguists distinguish between ChatGPT/AI and human writing? A study of research ethics and academic publishing. Research Methods in Applied Linguistics, 2(3), 100068. https://doi.org/10.1016/j.rmal.2023.100068
3. El-Dakhs, D. A. S., et al. (2026). A genre-based comparison of Chat-GPT-generated abstracts versus human-authored abstracts: Focus on Applied Linguistics research articles. Corpus Pragmatics, 10, 1. https://doi.org/10.1007/s41701-025-00210-8
4. Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. NPJ Digital Medicine, 6, 75. https://doi.org/10.1038/s41746-023-00819-6
5. Georgiou, G. P. (2025). Differentiating between human-written and AI-generated texts using automatically extracted linguistic features. Information, 16, 979. https://doi.org/10.3390/info16110979
6. Herbold, S., Hautli-Janisz, A., Heuer, U., et al. (2023). A large-scale comparison of human-written versus ChatGPT-generated essays. Scientific Reports, 13, 18617. https://doi.org/10.1038/s41598-023-45644-9
7. Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145
8. Kobak, D., González-Márquez, R., Horvát, E., & Lause, J. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances, 11(27). https://doi.org/10.1126/sciadv.adt3813
9. Wilson, W., & Rose, H. (2025). A genre, scoring, and authorship analysis of AI-generated and human-written refusal. Business and Professional Communication Quarterly. https://doi.org/10.1177/23294906251322890
10. Zhang, M., & Zhang, J. (2025a). Human-written vs. ChatGPT-generated texts: Stance in English research article abstracts. System, 134, 103842. https://doi.org/10.1016/j.system.2025.103842
11. Zhang, M., & Zhang, J. (2025b). Disciplinary variation of metadiscourse: A comparison of human-written and ChatGPT-generated English research article abstracts. Journal of English for Academic Purposes, 76, 101540. https://doi.org/10.1016/j.jeap.2025.101540

Prof. Dr. Dina Abdel Salam El-Dakhs
Dr. Dogan Yuksel
Dr. Muhammad Afzaal
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Languages is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• human-AI interaction
• AI communication
• linguistic analysis
• language use