Pragmatic Analysis of User Interaction with AI Chatbots in Digital Services
Kata Kunci:
Pragmatics, AI Chatbots, Digital Communication, Human-Computer Interaction, Conversational ImplicatureAbstrak
The rapid development of artificial intelligence technology has significantly transformed digital communication through the increasing use of AI chatbots in various sectors such as e-commerce, banking, healthcare, education, customer service, and government digital services. As human interaction with AI systems becomes more common, the effectiveness of chatbot communication has become an important issue, particularly in relation to contextual understanding, implied meaning, and conversational appropriateness. This study aims to analyze the pragmatic aspects of user interaction with AI chatbots in digital services, focusing on speech acts, contextual understanding, conversational implicature, politeness strategies, and communication effectiveness. The study employs a qualitative descriptive approach using discourse analysis and a pragmatic analysis framework. Data were collected from chatbot-user conversations across various digital platforms through observation, documentation, screenshot collection, and conversation transcription. The analysis is based on pragmatic theories including Speech Act Theory by John Searle, the Cooperative Principle and conversational implicature by H. P. Grice, and Politeness Theory by Penelope Brown and Stephen Levinson. The findings reveal that AI chatbots are generally capable of performing basic pragmatic functions effectively, particularly in structured and direct interactions involving information delivery and procedural guidance. However, chatbots still experience limitations in interpreting indirect requests, implied meanings, emotional nuances, sarcasm, and complex contextual relationships. Communication failures frequently occur when users employ implicit or emotionally sensitive expressions that require deeper pragmatic inference. Therefore, improving contextual awareness, emotional sensitivity, and inferential reasoning is essential for developing more natural, adaptive, and human-centered conversational AI systems in digital services.
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