“我比预期更喜欢Sunnie！”：探索用户对用于福祉支持的拟人化大语言模型对话智能体的期望与感知
"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support
AI总结：
本研究对比38名用户对两款LLM驱动的福祉推荐系统的期望与感知，发现拟人化设计的Sunnie在关系温暖度上优于非拟人基线，且两款系统实用性均超预期，为心理健康支持的交互设计提供参考。
摘要：
人机交互（HCI）研究领域长期以来一直关注用户对新技术（例如大语言模型（LLM））的实际体验与期望之间的差异。在本研究中，我们对比了38名用户对两款由LLM驱动的心理健康福祉干预活动推荐系统的初始期望与交互后的感知。两款系统都内置LLM来推荐个性化的福祉干预活动，但其中一款系统（Sunnie）通过外观、人设和自然对话等元素实现了拟人化对话交互设计。结果显示，两款系统的用户参与度都很高，且在实用性维度上都超出了用户预期，凸显了AI提供有用干预活动推荐的潜力。此外，Sunnie在关系温暖度上进一步优于非拟人化基线系统。这些发现表明，拟人化对话交互设计在心理健康支持场景中对营造温暖感可能尤为有效。
英文摘要：
The human-computer interaction (HCI) research community has a longstanding interest in exploring the mismatch between users' actual experiences and expectation toward new technologies, for instance, large language models (LLMs). In this study, we compared users' (N = 38) initial expectations against their post-interaction perceptions of two LLM-powered mental well-being intervention activity recommendation systems. Both systems have a built-in LLM to recommend a personalized well-being intervention activity, but one system (Sunnie) has an anthropomorphic conversational interaction design via elements such as appearance, persona, and natural conversation. Results showed that user engagement was high with both systems, and both systems exceeded users' expectations along the utility dimension, highlighting AI's potential to offer useful intervention activity recommendations. In addition, Sunnie further outperformed the non-anthropomorphic baseline system in relational warmth. These findings suggest that anthropomorphic conversational interaction design may be particularly effective in fostering warmth in mental health support contexts.
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。
