探索LLM驱动的多次会议的人机交互与大学生
Mauliana Mauliana1, Ashita Ashok1, Daniela Czernochowski2
1Robotics Research Lab, RPTU Kaiserslautern-Landau, Department of Computer Science, Kaiserslautern, Germany.
Frontiers in robotics and AI
|June 18, 2025
概括
这项研究探讨了用户与大型语言模型 (LLM) 驱动的社交人形机器人 (SHR) 的互动. 调查结果显示了持续的参与,但强调了技术挑战,影响了教育环境中的长期采用.
科学领域:
- 人与机器人的交互
- 人工智能的人工智能
- 社会机器人社会机器人社会机器人
背景情况:
- 社会类人形机器人 (SHRs) 越来越多地融入各种环境.
- 大型语言模型 (LLM) 为自然主义的人机交互提供了潜力.
- 了解用户的看法和采用因素对于SHR部署至关重要.
研究的目的:
- 调查多会话,开放域互动与LLM驱动的SHR对用户感知的影响.
- 评估用户在大学环境中采用此类机器人的意愿.
- 确定影响与SHRs长期社会参与的因素.
主要方法:
- 探索性研究涉及13名大学生与LLM驱动的SHR (EMAH) 交互,每周进行四次会议.
- 使用一个紧的开源LLM (Flan-T5-Large) 进行多轮对话.
- 采用混合方法测量:主观评分,行为观察和对话分析.
主要成果:
- 对社交,代理和参与的看法保持稳定;尽管反复暴露,但参与是持续的.
- 随着熟悉度的增加,感知到的动态性增加了,但干扰评级没有显著下降.
- 技术挑战 (响应时间,语音识别) 与研究中期互动指标的下降相关;之前的机器人经验没有预测采用意愿.
结论:
- 用LLM驱动的机器人可以保持开放域互动,但技术稳定性是关键.
- 长期的社会参与需要个性化的对话策略和期望管理.
- 提供了可操作的见解,以推动在教育环境中推进人形机器人部署.
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