Recentring young people in AI for mental health support: breaking the evidence-design feedback loop
- 1Faculty of Medicine, Imperial College London
- 2Faculty of Medicine and Health, The University of Sydney
- 3Charles Perkins Centre, The University of Sydney
Abstract
Young people are largely underrepresented in research underpinning AI-based mental health tools. AI systems are shaped by evidence bases that do not always fully incorporate young people’s opinions and perspectives. We propose a conceptual model, an evidence-design feedback loop in which exclusion from research may risk producing interventions that are less likely to be adopted by young people or meet their needs. These risks are amplified for young people from marginalised and underrepresented cultural contexts who face compounded exclusion, as the evidence base and design processes often lack cultural diversity. The relationships in this model are hypothesised from existing literature. This article argues that structural change is required across both research and industry to incorporate co-design with young people. AI developers should demonstrate meaningful involvement of young people from the earliest stages of development, and regulators should enforce higher regulatory standards in this respect. Researchers and AI developers should embed meaningful co-design with young people into the development of AI-based mental health tools which may help these technologies to be more relevant, safe and responsive to young people’s lived experiences.
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BibTeX
@article{sangha2026recentring,
title = {{Recentring young people in AI for mental health support: breaking the evidence-design feedback loop}},
author = {Jaspreet Kaur Sangha and Stephanie R. Partridge and Rebecca Raeside},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1954336},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1954336/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Recentring young people in AI for mental health support: breaking the evidence-design feedback loop
AU - Jaspreet Kaur Sangha
AU - Stephanie R. Partridge
AU - Rebecca Raeside
PY - 2026
DA - 2026/10/14
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1954336
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1954336/full
AB - Young people are largely underrepresented in research underpinning AI-based mental health tools. AI systems are shaped by evidence bases that do not always fully incorporate young people’s opinions and perspectives. We propose a conceptual model, an evidence-design feedback loop in which exclusion from research may risk producing interventions that are less likely to be adopted by young people or meet their needs. These risks are amplified for young people from marginalised and underrepresented cultural contexts who face compounded exclusion, as the evidence base and design processes often lack cultural diversity. The relationships in this model are hypothesised from existing literature. This article argues that structural change is required across both research and industry to incorporate co-design with young people. AI developers should demonstrate meaningful involvement of young people from the earliest stages of development, and regulators should enforce higher regulatory standards in this respect. Researchers and AI developers should embed meaningful co-design with young people into the development of AI-based mental health tools which may help these technologies to be more relevant, safe and responsive to young people’s lived experiences.
SN - 2296-2565
ER -