发表机构
University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过多方法分析，评估现有设计建议对AI伴侣设计的适用性，发现伦理与用户体验考量交织，需情境化应用，并总结九个设计原则领域以解释直接迁移的困难。
AI 中文摘要
随着基于大语言模型（LLM）的系统的迅速普及，AI伴侣已作为对话代理出现，其设计旨在培养情感联系，而非主要支持人类完成工具性任务。由于与AI伴侣的互动涉及关系性、情感性以及可能长期性的交互，其设计具有重要影响。先前的工作已为设计可信赖和关系型AI系统提供了指导，并已开始探讨AI伴侣的设计。然而，尽管此类工作为可能的设计解决方案提供了见解，但对于是什么使AI伴侣设计成为一个困难的设计问题，我们知之甚少。为考察这一挑战，我们评估了相邻领域现有设计建议在AI伴侣设计背景下的适用性。我们的多方法调查分四个阶段展开：文献综述、从业者共同分析、内部启发式评估和外部专家评估。在整个过程中，我们综合了九个设计原则领域，这些领域揭示了现有建议在适用于AI伴侣设计时存在的张力。我们的研究结果表明，伦理和以用户体验为导向的考量紧密交织，且通常需要情境敏感的应用。我们记录了一项系统性的、多方法的问题分析，该分析使用这些原则领域作为分析工具，以考察为何现有建议无法直接转移到AI伴侣情境中。
英文摘要
With the rapid proliferation of large language model (LLM)-based systems, AI companions have emerged as conversational agents designed to cultivate emotional connection rather than primarily to support humans in instrumental tasks. Because engagement with AI companions involves relational, emotional, and potentially long-term interactions, their design is consequential. Prior work has offered guidance for designing trustworthy and relational AI systems and has begun to examine design for AI companionship. However, while such work provides insights into possible design solutions, less is known about what makes AI companion design difficult as a design problem. To examine this challenge, we assessed the applicability of existing design recommendations from adjacent domains in the context of AI companion design. Our multi-method investigation unfolded across four phases: literature review, practitioner co-analysis, internal heuristic evaluation, and external expert assessment. Throughout this process, we synthesized nine design principle areas that surfaced tensions in the applicability of existing recommendations to AI companion design. Our findings show that ethical and UX-oriented considerations are deeply intertwined and often require context-sensitive application. We document a systematic, multi-method problem analysis that uses these principle areas as an analytic artifact to examine why existing recommendations cannot be directly transferred to AI companion contexts.
DOI：10.1016/j.ijhcs.2026.103940
