Between the Lines: Pragmatic Competence and Indirectness in AI-Driven Virtual Assistants

As artificial intelligence technology continues to advance, the distinction between natural and computer-generated speech is becoming increasingly blurred. Machines are beginning to grasp conversational nuances and infer meaning beyond the literal level. One of the most sophisticated features of human language is the ability to say one thing while intending something entirely different. As AI-driven personal virtual assistants are designed to assist users with progressively complex tasks and communicate in a more natural manner, it becomes crucial for them to recognize and interpret such underlying intentions in order to perform effectively. This paper explores the extent to which a personal virtual assistant can comprehend indirectness without losing sight of the primary communicative intent. Drawing on key pragmatic concepts such as speech acts, implicature, convention, and the cooperative principle, a theoretical framework is established to illuminate the human aspect of interaction. On the computational side, recent research in computer science—particularly within the field of natural language processing (NLP)—is reviewed to provide a concise overview of the core mechanisms involved in interpreting user input. The analysis focuses on five fundamental and frequently used direct commands, which are reformulated into various indirect requests through declarative and interrogative structures exhibiting different degrees of indirectness. This investigation aims to reveal how a virtual assistant processes these utterances and, in cases of misinterpretation, identify the source of the error. Furthermore, the study evaluates the applicability of a pragmatic framework in human–machine conversational analysis and examines how such an approach may inform the design and programming of virtual assistants.
Keywords: virtual assistant, pragmatics, indirect speech acts, natural language processing,
conversation analysis