arXiv cs.AI· Jayalakshmi Baskar, Vera C. Kaelin, Kaan Kilic, Helena Lindgren·· 3 天前AI 评分18
基于论证的老年人个性化故事生成：一种反思式混合 AI 架构
Argumentation-Based Person-Tailored Storytelling for Older Adults: A Reflective Hybrid AI Architecture
AI 导读
一项研究提出反思式故事生成智能体，整合知识图谱、用户建模与论证理论，为老年人定制叙事。Phase II 中 55 位老年人对 330 次评估里仅 30.6% 识别出预期目的；Phase III 显示完整系统人物设定落地率为 67.6%，高于无论证方案引导的 57.9%，但差异不显著。零样本论证挖掘与人工共识标注仅中等一致，定位为审查辅助而非验证器。
正文 · 原文
Abstract:This work investigates knowledge-driven, large language model (LLM)-based storytelling for older adults. We present a reflective storytelling agent integrating knowledge graphs, user modelling, and argumentation theory to guide person-tailored narratives. Argument mining supports inspection of argument structure, while persona grounding is assessed separately against structured information.
The study comprised three phases. Phase I involved 11 domain experts whose feedback informed system refinement. Phase II involved 55 older adults evaluating narratives based on predefined personas across inquiry, deliberation, and persuasion purposes and two creativity levels. Phase III compared the full system with an argumentation-scheme ablation and standard LLM baseline, and validated zero-shot argument mining against human-consensus annotations.
In Phase II, the intended purpose was recognised in 30.6% of 330 assessments; 33.3% indicated no clear purpose and 36.1% another purpose. Cultural relatability was descriptively associated with future-use intention, although 30 of 55 participants did not intend to use similar functionality.
In Phase III, 67.6% of applicable sampled statements were persona-grounded in the full system versus 57.9% without scheme guidance, a non-significant difference. Scheme guidance increased explicit premises without changing claim counts. The full system showed substantially greater sampled grounding than the standard LLM baseline. Zero-shot argument mining showed moderate agreement with human-consensus annotations, supporting its role as an inspection aid rather than a verifier. These findings highlight the potential and limitations of reflective, person-tailored LLM storytelling for older adults.
| Comments: | Included in Jayalakshmi Baskar's thesis |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | H.5.2; I.2.7 |
| Report number: | UMINF: 26.15 |
| Cite as: | arXiv:2605.10531 [cs.AI] |
| (or arXiv:2605.10531v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.10531 arXiv-issued DOI via DataCite |
Submission history
From: Jayalakshmi Baskar [view email]
[v1]
Mon, 11 May 2026 13:17:31 UTC (669 KB)
[v2]
Sun, 11 Oct 2026 12:58:02 UTC (752 KB)
来源：arXiv cs.AI · arxiv.org