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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.19733 |
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| _version_ | 1866915893680799744 |
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| author | Wang, Zilong Chen, Nan Qiu, Luna K. Yue, Ling Guo, Geli Ou, Yang Jiang, Shiqi Yang, Yuqing Qiu, Lili |
| author_facet | Wang, Zilong Chen, Nan Qiu, Luna K. Yue, Ling Guo, Geli Ou, Yang Jiang, Shiqi Yang, Yuqing Qiu, Lili |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19733 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations Wang, Zilong Chen, Nan Qiu, Luna K. Yue, Ling Guo, Geli Ou, Yang Jiang, Shiqi Yang, Yuqing Qiu, Lili Artificial Intelligence 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. |
| title | ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.19733 |