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Experts' Perception of AI-Generated Personalised Stories for Health Promotion
Jayalakshmi Baskar1, Vera C Kaelin1, Helena Lindgren1
1Department of Computing Science, Umeå University, 901 87 Umeå, Sweden.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study explored using large language models (LLM) for personalized health stories. Experts saw potential benefits and risks, emphasizing careful design for individual needs in health reflection.
Area of Science:
- Digital Health
- Artificial Intelligence in Healthcare
- Health Behavior Change
Background:
- Personalized interventions are key for health behavior change.
- Storytelling systems offer a novel approach to health reflection.
- Large language models (LLMs) enable sophisticated content generation.
Purpose of the Study:
- To explore the potential of LLM-generated, person-tailored stories for health reflection.
- To understand expert perceptions of the benefits and risks of such systems.
- To inform the design of future health storytelling functionalities.
Main Methods:
- Qualitative study employing formative evaluation.
- Involved ten domain experts.
- Explored perceived purposes, benefits, and risks of the storytelling system.
Main Results:
- Domain experts recognized opportunities for targeted health reflection.
- Concerns were raised regarding the risks of LLM-based stories if not individualized.
- Careful design is crucial for adapting stories to user needs.
Conclusions:
- LLM-powered storytelling shows promise for supporting health reflection.
- Personalization and careful design are critical to mitigate risks.
- Findings will guide development for future studies, particularly with older adults.
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