By identifying ways that AI can empower practitioners, and by exploring the gaps in provision and governance, we can map out different routes to support the early-years sector with this technology. For example, automating repetitive tasks to allow practitioners to spend more time with children, or making children’s activities more personalised  to improve their outcomes. Ultimately, we believe this can build more high-quality and effective childcare settings, provision and experiences.

The development of AI and education technology in the early-years sector represents a significant opportunity to link new technological solutions to substantial problems felt by practitioners and the wider system. AI’s potential for improving content generation, efficiency, personalisation, knowledge extraction, data analysis and generation could help early-years professionals to deliver provision and support for children.

We will explore the potential of AI by identifying the most relevant technological developments and how they can be used for the benefit of early-years practitioners and evidence-based interventions. We need to map out a desirable, feasible and viable path for adoption and scalability to have the greatest impact across the early-years sector.

We will also explore how adoption could be influenced by funding flows, regulatory changes, and digital readiness in the sector. We need to understand the whole system to make decisions about where and how we can best support the spread and scaling of AI for disadvantaged children.

The first three months of this project are in exploration and discovery to define Nesta’s role in leveraging AI to improve child and family outcomes.

We have split this work into a number of workstreams, across three phases.

Phase one

The first phase involves identifying key pain points to solve for practitioners across the early-years sector. We will begin by mapping the system and its stakeholders to pinpoint relevant problems that have the potential for AI or digital tech intervention.

To do this, we will be engaging with practitioners across different settings, including maintained & unmaintained nursery settings, preschools, day nurseries, children’s centres and the health visiting service. We will intentionally be broad with our early discovery, looking at the wider landscape and then focusing on trial solutions and approaches that support practitioners.

We will also be exploring the findings from Nesta’s Innovation Sweet Spots programme on potential developments in early-years tech. Using this dataset, we want to define potential products or services to test with early-years stakeholders to understand potential benefits and drawbacks to validate our hypotheses and develop solutions

Phase two

We will use this insight and market scanning to create prototypes and concepts to test with practitioners and the wider early-years system. This will focus on a subset of practitioner groups and stakeholders to continue building evidence against the potential benefits of AI, and Nesta’s role in building its adoption.

Phase three

In phase three, we will continue to build our insight and learnings on the opportunities and barriers for adoption of AI in the early years. We will use this phase to synthesise our learnings and map feasible, viable routes for scaling, and where Nesta can support the early-years sector. We’ll test our assumptions through this mapping process with stakeholders and feed this into further learning and experimentation.

Following this initial project, we plan to field test three to five initiatives to enable the early-years ecosystem to leverage AI for child and family outcomes.

If you’re part of the early-years sector or have a perspective on potential AI developments that could benefit the sector, we’d love to hear from you.

We’re particularly interested in speaking to practitioners and those who work across the early years, as well as AI developers, investors and people who want to share their insight on the future of the AI ecosystem. If you’re interested in participating in this work, please email [email protected] to arrange an interview.