Dr Paulina Schenk, University College London

This project will explore how behavioural science artificial intelligence tools can support more inclusive behaviour change communications and be integrated into the everyday work of UK public health teams.  

The work will happen in two phases. First, a diverse Patient and Public Involvement and Engagement (PPIE) panel will be established, including people with lived experience of non-communicable diseases and members of underrepresented communities. The panel will take part in a workshop to review existing campaigns, highlight what makes them feel inclusive, and identify where certain groups might be overlooked or misrepresented. 

Second, local public health teams will be given a prototype behavioural science artificial intelligence tool (evorAI) to interact with during “think-aloud” interviews, providing real-time feedback on usability, relevance, and fit with their day-to-day work. evorAI, developed by Applied Behaviour Change (ABC), analyses publicly available text - such as blogs and social media posts - using established behavioural science frameworks. This produces structured summaries of what influences people’s behaviours, which could then be used to support the design of public health communication campaigns.  

Insights from the “think-aloud” interviews, alongside those from the PPIE workshop, will inform a co-design session to pinpoint features that ensure these tools can be seamlessly integrated into the everyday work of UK public health teams, and develop processes for bias detection and mitigation. 

The project will produce open resources, academic outputs, and a practitioner–researcher–public network, laying the groundwork for more inclusive AI tools in public health. 

Deliverables/Outputs

(1) Feedback to guide the co-creation workshop

(2) infographics summarising discussions for use in future funding applications. 

Insights will focus on inclusivity and bias detection, shaping practitioner workshop priorities. 

The co-creation workshop will result in a peer-reviewed publication outlining design specifications and inclusive principles, alongside conference presentations (e.g., UCL Centre for Behaviour Change Conference) to share findings with academic and practitioner audiences.  Both phases will contribute to establishing a practitioner–researcher–public network for future tool development and testing.  All publicly shareable materials will be stored in an open repository (e.g., OSF), extending the project’s impact beyond immediate partners. 

Outcomes 

Embedding impact from project inception, we will ensure findings directly inform the next generation of AI behavioural science tools for public health. Outputs will guide both the refinement of evorAI and broader sector learning on inclusive AI design. The practitioner–researcher–public network established will sustain collaboration, feedback, and knowledge exchange beyond the life of the project. Ultimately, this work will support the creation of public health communication campaigns that are evidence-based, culturally sensitive, and equitable - contributing to improved health outcomes in communities across the UK.