Artificial intelligence in applied family research involving families with young children: A scoping review

Objective: This scoping review systematically examined the applied family science literature involving families raising young children to understand how relevant studies have applied artificial intelligence (AI)-facilitated technologies. Background: Family research is exploring the application of AI. However, there is a critical need for a review study that systematically examines the varied use of AI in applied family science to inform family practitioners and policymakers. Method: Comprehensive literature searches were conducted in nine databases. Of the 10,022 studies identified, 21 met inclusion criteria: peer-reviewed journal article; published between 2014–2024; written in English; involved the use of AI in collecting data, analyzing data, or providing family-centered services; included families raising young children 0–5 years; and was quantitative in analysis. Results: Most studies focused on maternal and child health outcomes in low- and middle-income countries. All studies identified were in the AI use domain of data analysis, with 76% of the studies having a focus on identifying the most important predictors. Random forest performed as the best machine learning model. Only one study directly mentioned the ethical use of AI. Conclusion: Overall, the applied family science evidence base that employs AI is limited in size and scope, with most studies using AI for data analysis purposes with limited ethical considerations. Implications: AI models in applied family science can inform family services and policies aimed at promoting family and child health. However, thoughtful consideration of AI ethics and fairness is needed to prevent the negative social impacts of AI on marginalized groups of families and their young children.