Persuasive language shapes communication across disciplines and everyday life. As large language models (LLMs) become increasingly integrated into these spheres, understanding persuasion now encompasses both human and machine discourse. This introduction examines how persuasive language operates across diverse contexts by analyzing the interactional frameworks of human and AI communication. It also explores how persuasion emerges in human-AI exchanges and how these insights can inform language education and communication practices. Drawing on perspectives from linguistics, computer science, journalism, and communication studies, it presents persuasion as both a rhetorical and interactional process shaped by technology. Ultimately, it aims to deepen understanding of how AI transforms persuasive practices and to promote greater awareness of persuasion in language learning.

This study investigates how connectives However and While, signaling contrast/ concession to construct stances, are distributed by AI chatbots in task-based argumentations. The corpus, comprising 13,482 words of chatbot-produced discourse, was analyzed to examine the connectives’ sentence positions and their relation to content-, writer-, and reader-oriented propositions, based on an integrated framework of Hyland’s (2005) framework and Thetela’s (1997) evaluative-entity framework. A total of 124 tokens of However and While were extracted, excluding tokens whose stance and cohesive functions can’t be clearly interpreted. Results show sentence-initial However (N=40) and sentence-initial while (N=59) are the primary devices for asserting a writer-oriented stance, signaling evaluation, claim or counter-claim. Sentence-initial while are more frequently used to frame a factual premise before projecting writer orientation. As to sentence-medial while, both preceding and subsequent clauses are often presented content-oriented propositions, indicating achieving cohesion is prioritized over expressing an evaluative stance. This study concludes that the use of these connectives, strategically applied in AI-human argumentations, shows how connectives contribute to manage stance construction and discourse coherence.

The measurement of conceptual similarity in a hierarchical structure has been proposed by studies such as Wu and Palmer (1994) which have been summarized and evaluated in Budanisky and Hirst (2006). The present study applies the measurement of conceptual similarity to conceptual metaphor research by comparing concreteness of ontological resource nodes to several prototypical concrete nodes selected by human subjects. Here, the purpose of comparing conceptual similarity between nodes is to select a concrete sense for a word which is used metaphorically. Through using WordNet-SUMO interface such as SinicaBow (Huang, Chang and Lee, 2004), concrete senses of a lexicon will be selected once its SUMO nodes have been compared in terms of conceptual similarity with the prototypical concrete nodes. This study has strong implications for the interaction of psycholinguistic and computational linguistic fields in conceptual metaphor research.