O papel da hesitação na interação humano-máquina: uma análise no atendimento via chat

Abstract

This research aims to describe and analyze hesitation in interactions carried out through customer service chats that use Artificial Intelligence (AI) systems, based on the Interactive Textual Perspective (Jubran, 2006). The investigation focuses on identifying and describing the linguistic markers of hesitation in interactions mediated by three types of interlocutors: human agents, users, and a chatbot, as well as verifying how these markers manifest and impact the perception of fluency, comprehension, and humanization in dialogue. The study is grounded in the understanding of AI as a technical field that also seeks to develop agents capable of simulating human behavior (Russell; Norvig, 2016; Nilsson, 1998), with particular focus on automated conversational systems (Jurafsky; Martin, 2023), such as chatbots, which constitute hybrid environments between orality and writing. In this context, hesitation is defined as an interactive-discursive phenomenon with a textual function, capable of signaling doubt, organizing discourse, and seeking cooperation, as discussed by Marcuschi (1999), Marcuschi (2000). The analyzed data are drawn from a corpus of real customer service interactions conducted by a nationally recognized company that authorized the use of the data. The methodology involves the qualitative and quantitative categorization of hesitation markers according to linguistic criteria, including frequency of hesitations, type of hesitation, communicative context, impact on interactions, user response, human vs. AI comparison, conversation length, pragmatics of hesitation, cognitive aspect of hesitation, and interactional aspect of hesitation. The analysis shows that hesitation, when more frequent among human interlocutors—mainly in functional words and hesitation markers—is associated with interactions that generate effects of empathy and cooperation from users. In contrast, the absence or superficial simulation of this phenomenon in AI turns compromises the continuity and naturalness of the interaction. By situating hesitation as an important element in the construction and fluency of conversations mediated by humans and robots, this research explores and connects the subject to the field of technology and may contribute to studies on the humanization of AI in automated customer service, reinforcing the importance of integrating linguistic, pragmatic, and technological aspects in the design of conversational systems that are more sensitive to natural language.