The National EdgeAI Hub has launched a pump priming call to encourage research into Edge AI applications from non-technical perspectives. This initiative seeks to engage disciplines such as social sciences, medical sciences, and related fields to explore the societal, ethical, and practical implications of deploying AI at the edge. By supporting such projects, the Hub aims to broaden the impact of Edge AI beyond technical innovation, fostering insights into human, organisational, and domain contexts where these technologies can deliver meaningful benefits.

We strongly encourage applicants to develop proposals that foster interdisciplinary collaboration, bringing together expertise from diverse fields. Interdisciplinary approaches are essential for addressing the technical, societal, and ethical dimensions of Edge AI adoption and impact. In addition, we actively promote collaboration between academia and industry, as well as partnerships with public sector and civil society organisations. These collaborations help ensure that research is both innovative and practically relevant, enabling real-world application, knowledge exchange, and pathways to impact.

We encourage applicants to submit projects that align with our primary focus areas of smart transport, health, energy, manufacturing, and smart cities. However, proposals are not limited to these domains. We welcome innovative ideas from other sectors where Edge AI can deliver meaningful impact, particularly those that demonstrate strong interdisciplinary collaboration and partnerships between academia and industry.

Indicative areas for Edge AI applications include:

• Agriculture and food systems: precision farming, crop monitoring, and supply chain optimisation
• Environmental monitoring: air quality, water management, and climate resilience solutions
• Public safety and emergency response:  real-time analytics for disaster management and security
• Retail and logistics:  inventory management, predictive demand, and autonomous delivery systems
• Education and training:  adaptive learning platforms and immersive technologies in relation to Edge applications
• Healthcare and wellbeing: remote patient monitoring, diagnostics, and personalised care
• Energy and utilities: smart grids, predictive maintenance, and energy efficiency optimisation
• Industrial automation: robotics, autonomous vehicles, predictive maintenance, and process optimisation

This call invites proposals to three stands discussed below.

Strand 1: Technology adoption and acceptance

This strand investigates how Edge AI can be effectively introduced and normalised within diverse organisational and societal contexts. It considers user experience, organisational capacity, governance readiness, and ecosystem maturity required for successful implementation. It also examines pathways from pilots to scaled deployment, highlighting barriers, enablers, and change management strategies that shape adoption trajectories.

Indicative areas to consider:

• Trust, reliability, and perceived usefulness of Edge AI in frontline settings
• User experience design for edge interfaces and decision support
• Organisational readiness, capabilities, and reskilling for edge-enabled workflows
• Stakeholder engagement, co creation, and participatory design approaches
• Procurement models, vendor lock in risks, and interoperability requirements
• Policy, regulatory, and standards landscape for edge deployments
• Change management, incentives, and adoption roadmaps from pilots to scale

Strand 2: Benefits realisation and technology assessment and impact

This strand focuses on how value from Edge AI is measured, evidenced, and sustained over time. It encompasses frameworks for evaluating outcomes across economic, social, and environmental dimensions, with attention to system wide impacts. It seeks methods for robust evaluation, continuous improvement, and the translation of evidence into practice and policy.

Indicative areas to consider:

• Outcome measurement frameworks spanning efficiency, safety, and quality
• Cost benefit analysis, total cost of ownership, and return on investment
• Impact on service delivery, workforce productivity, and user satisfaction
• Clinical and public health outcomes for medical applications and care pathways
• Education, social care, and public services performance indicators
• Environmental sustainability, energy use at the edge, and carbon accounting
• Comparative assessments versus cloud-based AI and hybrid models
• Longitudinal studies, real world evidence, and post deployment monitoring
• Data quality, bias detection, and performance drift management
• Translating evaluation findings into commissioning, policy, and scaling decisions

Stand 3: Responsible innovation

This strand ensures Edge AI is designed, governed, and deployed in ways that are ethical, inclusive, and aligned with societal values. It foregrounds accountability, transparency, and meaningful oversight across the lifecycle, from data collection to model updates in edge environments. It also addresses resilience against cyber threats and safeguards for vulnerable populations.

Indicative areas to consider:

• Ethical principles operationalised in edge system design and governance
• Privacy preservation, data minimisation, and on device data stewardship
• Security by design, adversarial robustness, and cyber resilience at the edge
• Fairness, accessibility, and inclusive design for diverse user groups
• Human in the loop oversight, auditability, and explainability of decisions
• Safety cases, assurance processes, and certification pathways for edge AI
• Legal and regulatory compliance including accountability and liability models
• Anticipating unintended consequences and societal risks
• Community engagement, public deliberation, and responsible data sharing
• Lifecycle governance for updates, model drift, and end of life decommissioning
• Equity of access and digital inclusion across regions and communities

For all information related to eligibility and applying please download the funding call.