This article is based on a research study. A research study is when researchers collect and analyse information to answer a question in a careful and organised way.

This study describes how researchers developed resources to support choosing and using outcome measures that reflect what matters to children and families. Outcome measures are tools that help us understand if supports have made a difference over time. The work supported the National Best Practice Framework for Early Childhood Intervention, which aims to improve outcomes for children and families.

The researchers used both traditional research methods and artificial intelligence tools. AI helped quickly find, sort and summarise information from many sources. Researchers led the work and checked information at each step to make sure it was accurate and useful. The team followed a clear process and worked together to solve challenges as they went.

The study showed that AI can support this type of work when used carefully. It helped bring together research evidence and practical information about outcome measures. Researchers also recognised that AI is not always accurate, so careful checking and judgement were needed throughout. The resources developed make it easier for practitioners and families to choose and use outcome measures that reflect what matters to them.

Citation:

Long, S. H., D’Aprano, A. L., Lami, F., Wilson, M. R., Knight, S. J., Yates, M. J., & Imms, C. (2026). Guidance on artificial intelligence use for rapid evidence mapping of early childhood intervention outcome measures. Developmental Medicine & Child Neurology. 2026;00:1–9 https://doi.org/10.1111/dmcn.70323

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