aiqbench analysis
This research presents a reflective hybrid AI architecture that combines knowledge graphs, user modeling, and argumentation theory to generate tailored stories for older adults. The study evaluates the system's ability to ground narratives in specific personas and inspect argument structures, revealing mixed results where scheme guidance improved premise explicitness but did not significantly boost persona grounding over standard LLM baselines. These findings offer a critical look at the current capabilities and limitations of specialized LLM applications designed for demographic-specific engagement.
Argumentation-Based Person-Tailored Storytelling for Older Adults: A Reflective Hybrid AI Architecture
arXiv:2605.10531v2 Announce Type: replace 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2605.10531 [cs.AI]
(or arXiv:2605.10531v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.10531 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 319 words
Related coverage

Hint-based CoT faithfulness evals still mostly work on Claude
LessWrong · Jul 30, 2026
Models Can't Remember Their Training. Neither Can You.
LessWrong · Jul 19, 2026
The J-Space Debate, Agent Swarms, and Pacing Frontier AI - Digital Minds Newsletter #4
LessWrong · Sep 18, 2026

Why I Left Google DeepMind
LessWrong · Jul 15, 2026