首页|Researchers at University of South Carolina Target Androids (Chatbots On the Fro ntline: the Imperative Shift From a 'one-size-fitsall' Strategy Through Convers ational Cues and Dialogue Designs)

Researchers at University of South Carolina Target Androids (Chatbots On the Fro ntline: the Imperative Shift From a 'one-size-fitsall' Strategy Through Convers ational Cues and Dialogue Designs)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics - Androids. According to news reporting from Columbia, South Carolina, by NewsRx journalists, research stated, “The lack of transparency in AI-related technology poses challenges in identifying elements that influence conversation fluency with chatbot. Drawing from media richness, task-technology fit, and flow theories, we propose an integrated framework to investigate how chatbots’ human oid characteristics affect users’ process fluency.” The news correspondents obtained a quote from the research from the University o f South Carolina, “Furthermore, we explore boundary conditions of dialogue chara cteristics, including conversation types (topic-related vs. task-related) and in teraction mechanisms (free-text vs. button-based) that amplify or disrupt such f low-like experience in conversation. Two separate scenario-based experimental st udies were conducted to explore two chatbot humanoid characteristics, human-like cues (Study 1) and tailored responses (Study 2). Results suggest that a match b etween chatbot’s humanoid and dialogue characteristics can increase fluency in c omprehending the message, enhancing customer satisfaction and usage intention. S pecifically, chatbots with humanoid conversational cues promote more flow-like m essages in topic-related conversation or free-text interaction.”

ColumbiaSouth CarolinaUnited StatesNorth and Central AmericaAndroidsEmerging TechnologiesMachine LearningR oboticsTechnologyUniversity of South Carolina

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.16)