Lived Experience in Dialogue: Co-designing Personalization in Large Language Models to Support Youth Mental Well-being

• URL: http://arxiv.org/abs/2511.05769v1
• Date: Fri, 07 Nov 2025 23:53:36 GMT
• Title: Lived Experience in Dialogue: Co-designing Personalization in Large Language Models to Support Youth Mental Well-being
• Authors: Kathleen W. Guan, Sarthak Giri, Mohammed Amara, Bernard J. Jansen, Enrico Liscio, Milena Esherick, Mohammed Al Owayyed, Ausrine Ratkute, Gayane Sedrakyan, Mark de Reuver, Joao Fernando Ferreira Goncalves, Caroline A. Figueroa,
• Abstract summary: This study elicits community perspectives on how LLMs can facilitate more meaningful personalization to support youth mental well-being.<n>We identify three themes: person-centered contextualization responsive to momentary needs, explicit boundaries around scope and offline referral, and dialogic scaffolding for reflection and autonomy.<n>Our findings demonstrate how lived experience can be operationalized to inform design features in LLMs, which can enhance the alignment of LLM-based interventions with the realities of youth and their communities.
• Score: 7.568079306155379
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Youth increasingly turn to large language models (LLMs) for mental well-being support, yet current personalization in LLMs can overlook the heterogeneous lived experiences shaping their needs. We conducted a participatory study with youth, parents, and youth care workers (N=38), using co-created youth personas as scaffolds, to elicit community perspectives on how LLMs can facilitate more meaningful personalization to support youth mental well-being. Analysis identified three themes: person-centered contextualization responsive to momentary needs, explicit boundaries around scope and offline referral, and dialogic scaffolding for reflection and autonomy. We mapped these themes to persuasive design features for task suggestions, social facilitation, and system trustworthiness, and created corresponding dialogue extracts to guide LLM fine-tuning. Our findings demonstrate how lived experience can be operationalized to inform design features in LLMs, which can enhance the alignment of LLM-based interventions with the realities of youth and their communities, contributing to more effectively personalized digital well-being tools.

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