情感与会话预测人类与机器人互动中重新参与的因素——一项以学生为中心的研究，使用人形社交机器人
Affective and Conversational Predictors of Re-Engagement in Human-Robot Interactions -- A Student-Centered Study with A Humanoid Social Robot
AI总结：
本研究发现，机器人对话的有趣性和自然性是影响用户重新参与的主要因素，而非情感特征。
摘要：
人形社交机器人日益融入日常生活，使持续的用户参与成为其有效性和接受度的关键因素。尽管先前的研究多关注情感评价或拟人化设计，但对动态会话质量与感知机器人特征在决定用户重新参与大型语言模型（LLM）驱动社交机器人意图中的相对影响仍知之甚少。在本研究中，68名参与者与Nadine人形社交机器人进行了开放式对话，完成预对话和后对话调查以评估机器人感知、会话质量及重新参与意图的变化。结果显示，口头互动显著提升了机器人感知特征，统计学上显著增加愉快性（p<.0001）和亲和力（p<.0001），并减少令人不适感（p<.001）。然而，这些情感变化在多重回归模型中并非用户重新参与意图的强且独特的预测因素。相反，参与者对机器人对话有趣性（β=0.60，p<.001）和自然性（β=0.31，p=0.015）的感知成为重新参与意图最显著和稳健的独立预测因素。总体而言，结果表明会话质量，特别是感知的有趣性和自然性，是重新参与的主要驱动因素，表明LLM驱动的机器人设计应优先考虑吸引人、自然的对话，而非情感印象管理或拟人化线索。
英文摘要：
Humanoid social robots are increasingly present in daily life, making sustained user engagement a critical factor for their effectiveness and acceptance. While prior work has often examined affective evaluations or anthropomorphic design, less is known about the relative influence of dynamic conversational qualities and perceived robot characteristics in determining a user's intention to re-engage with Large Language Model (LLM)-driven social robots. In this study, 68 participants interacted in open-ended conversations with the Nadine humanoid social robot, completing pre- and post-interaction surveys to assess changes in robot perception, conversational quality, and intention to re-engage. The results showed that verbal interaction significantly improved the robot's perceived characteristics, with statistically significant increases in pleasantness ($p<.0001$) and approachability ($p<.0001$), and a reduction in creepiness ($p<.001$). However, these affective changes were not strong and unique predictors of users' intention to re-engage in a multiple regression model. Instead, participants' perceptions of the interestingness ($β=0.60$, $p<.001$) and naturalness ($β=0.31$, $p=0.015$) of the robot's conversation emerged as the most significant and robust independent predictors of intention to re-engage. Overall, the results highlight that conversational quality, specifically perceived interestingness and naturalness, is the dominant driver of re-engagement, indicating that LLM-driven robot design should prioritize engaging, natural dialogue over affective impression management or anthropomorphic cues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
