用可控对话让AI陪老人玩认知训练游戏，还能结合个人生活记忆。
ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations
- 把认知训练设计成可复用的对话谜题模板，支持个性化生成
- 接入用户生活日志，实现基于真实记忆的回忆练习
- 32名50岁以上参与者试用，初步验证可行性
全球老龄化催生对可扩展且吸引人的认知干预需求。计算机化认知训练（CCT）是一种有前景的非药物手段，但许多无监督程序依赖僵化的手工设计谜题，难以个性化且影响参与度。大语言模型（LLMs）提供更自然的交互，但其开放生成特性难以满足认知训练所需的结构化任务要求。我们提出ReMe，一个基于网页的框架，通过可控的LLM对话实现认知训练的支架式支持，解决了传统CCT内容僵化与对话可控性不足的问题。ReMe包含模块化谜题引擎，将训练活动表示为由结构化模板和约束规则定义的可复用谜题组，支持快速构建基于对话的词汇游戏与基于用户上下文的个性化任务。通过整合个人生活日志，ReMe支持通过引导检索与渐进提示进行情景记忆练习。一项包含32名50岁以上成年人的社区试点提供了初步可行性信号。
原文摘要 · Abstract (English)
Global aging calls for scalable and engaging cognitive interventions. Computerized cognitive training (CCT) is a promising non-pharmacological approach, yet many unsupervised programs rely on rigid, hand-authored puzzles that are difficult to personalize and can hinder adherence. Large language models (LLMs) offer more natural interaction, but their open-ended generation complicates the controlled task structure required for cognitive training. We present ReMe, a web-based framework that scaffolds cognitive training through controllable LLM-mediated conversations, addressing both rigidity in conventional CCT content and the need for conversational controllability. ReMe features a modular Puzzle Engine that represents training activities as reusable puzzle groups specified by structured templates and constraint rules, enabling rapid development of dialogue-based word games and personalized tasks grounded in user context. By integrating personal life logs, ReMe supports Life Recall activities for episodic-memory practice through guided retrieval and progressive cues. A community pilot with 32 adults aged 50+ provides initial feasibility signals.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。