In the UK, children from lower-income backgrounds tend to enter school with a lower level of attainment compared to their peers. Nesta’s fairer start mission is dedicated to narrowing the outcome gap between children growing up in disadvantage and the national average. Alongside finding ways to improve the home learning environment, helping local authorities to innovate with their services, and exploring the possibilities to support family incomes, we also want to consider the role of emerging technologies

Could trends such as recent advances in artificial intelligence and growing venture funding for child-focussed products and services pave the way for novel, impactful innovations for supporting child development?

We will address this question by using our data-driven methodology for analysing data related to research and development, to identify and assess imminent impactful innovations and technologies.

By identifying impactful areas of research and development, we can contribute to a vision for how emerging technologies could support child development in a more precise and timely way.

When it comes to early childhood development, the earlier an intervention takes place – whether that be for special educational needs, or facilitating improvements in the home environment – the better that child’s chances of excelling at school.

Existing methods of assessing child development are mostly low- or no-tech, which makes them accessible but also comes with certain limitations. Existing standardised developmental screening measures such as the Ages & Stages Questionnaire (ASQ-3), used in England and Scotland, rely on either professionals or parents completing assessment forms at specific points in time. Nesta’s ongoing research into the ASQ-3 has surfaced concerns in the academic community about its specificity and sensitivity, alongside practical challenges related to wide variation in completion methods and rates.

Our previous work on parenting tech has highlighted new technologies such as AI-powered speech recognition for young children (used in products such as EarlyBird to detect dyslexia). Our recent investments analysis found that special educational needs is one of the most rapidly growing areas of early years investment. Now, we will get a bigger-picture view of the research and development landscape, to find out where the sweet spots of innovation may be and what early childhood development monitoring could look like in the future.

We will uncover trends in innovations and technologies that detect or anticipate child development needs (for children aged 0-5) in a more timely and granular fashion than is presently possible. We also aim to identify innovations beyond screening that can help families manage these development needs.

We will achieve this by exploring extensive research and patent datasets and employing natural language processing methods. We will utilise machine learning approaches such as supervised learning and generative AI to categorise the various different technologies and innovations, and describe research trends around the different categories.

Besides trends analysis, we will also consider the potential impact of these technologies on supporting child development while also being mindful of their potential risks. We will need to consider concerns about emerging technologies and AI, ranging from issues around data privacy to inclusivity for children from minoritised communities.

Once we’ve made the first step of surfacing innovation trends, we hope to continue this work in subsequent stages by exploring the attitudes of caregivers towards any innovations we identify and by conducting a more in-depth analysis of the potential impacts and future consequences of those innovations.