[2410.19733] ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations

Computer Science > Artificial Intelligence arXiv:2410.19733 (cs) [Submitted on 25 Oct 2024 (v1), last revised 27 Mar 2026 (this version, v2)] Title:ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations Authors:Zilong Wang, Nan Chen, Luna K. Qiu, Ling Yue, Geli Guo, Yang Ou, Shiqi Jiang, Yuqing Yang, Lili Qiu View a PDF of the paper titled ReMe: Scaffolding Personalized Cognitive Training via Controllable LLM-Mediated Conversations, by Zilong Wang and 8 other authors View PDF HTML (experimental) Abstract: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...