当AI“发挥作用”时，何时需要提供帮助？：围绕老年人使用大语言模型（LLM）的代际支持
When AI "Works," When Does Help Begin?: Intergenerational Support Around Older Adults' LLM Usage
AI总结：
该研究针对6名老年人和7名年轻人开展定性研究，发现代际LLM支持存在信号不足、知识无法复用的问题，据此提出代际LLM支持的相关设计启示。
AI中文摘要：
大语言模型（LLM）正成为日常生活的一部分，包括老年人（OAs）的日常生活。老年人通常向年轻家庭成员学习数字技术，这些家庭成员传统上充当“温暖专家”，提供可信赖且个性化的操作帮助。LLM扩展了这一角色：家庭支持者还可帮助老年人判断LLM的合适用途、考虑应披露的信息、评估输出的可信度，并决定AI生成的建议何时可安全执行。我们对6名老年人和7名年轻人（YAs）开展了一项形成性定性研究，采用半结构化访谈和基于场景的出声思维活动。老年参与者表示，使用LLM可减轻他们对家庭的持续依赖，同时将家庭保留为可选择性调用的支持渠道。但由于LLM很少出现明显的操作故障，年轻人对于何时真正需要支持的信号有限，转而依赖老年人的部分披露信息，通过一般性警告和自我设定的行动边界协商干预。结果，家庭支持常解决即时问题，却未留下可重复使用的校准知识供未来使用。基于这些发现，我们提出了代际LLM支持的设计启示，例如经同意的帮助请求、保留老年人任务自主权的学习型家庭支持。
英文摘要：
LLMs are becoming part of everyday life, including for older adults (OAs). OAs often learn digital technologies with younger family members, who have traditionally served as "warm experts" providing trusted and personalized operational help. LLMs expand this role: family supporters may also help OAs judge appropriate uses, consider what information to disclose, assess the credibility of outputs, and decide when AI-generated advice is safe to act on. We conducted a formative qualitative study with six OAs and seven younger adults (YAs), using semi-structured interviews and scenario-based think-aloud activities. OA participants described using LLMs to lighten their recurring reliance on family, while preserving family as a selectively invoked support channel. However, because LLMs rarely produced visible operational breakdowns, YAs had limited signals for when support was actually needed. Instead, YAs relied on OAs' partial disclosures and negotiated intervention through general warnings and self-imposed action boundaries. As a result, family support often solved an immediate problem without leaving reusable calibration knowledge for future use. Based on these findings, we propose design implications for intergenerational LLM support (e.g., consentful help requests, learning-oriented family support that preserves OA task ownership).
