jats:titleAbstract</jats:title>jats:sec<jats:label />jats:pThis paper contributes to the scarce literature on factors affecting EdTech use in households. These factors were considered through exploratory mixed‐methods analyses of cross‐sectional data on Kenyan girls and caregivers, captured during the COVID‐19 pandemic. Quantitative analysis of the child dataset (jats:italicn</jats:italic> = 544) suggested the importance of both structural factors—such as technology hardware availability—and non‐structural factors—including caregiver permission. Findings were supported by a thematic analysis of interview data from girls' caregivers (jats:italicn</jats:italic> = 58), which emphasised the role they play in girls' use of EdTech. Interviews also highlighted numerous caregiver concerns with EdTech, related to the relevance and rigour of educational content, the possibility of children accessing age‐inappropriate material and child health (especially eyesight). Policy makers could alleviate these concerns by providing guidance on EdTech use and clearly signalling their approval of verified initiatives.</jats:p></jats:sec>jats:sec<jats:label />jats:p <jats:boxed-text content-type="box" position="anchor">jats:captionjats:titlePractitioner notes</jats:title></jats:caption>jats:pWhat is already known about this topic<jats:list list-type="bullet"> jats:list-itemjats:pEdTech can benefit girls' education, yet there are various barriers to it being used.</jats:p></jats:list-item> jats:list-itemjats:pExisting research shows clearly that EdTech use can be impeded by structural factors (eg, hardware ownership).</jats:p></jats:list-item> jats:list-itemjats:pHowever, we find insufficient empirical evidence on the role of non‐structural or behavioural factors.</jats:p></jats:list-item> </jats:list> </jats:p>jats:pWhat this paper adds<jats:list list-type="bullet"> jats:list-itemjats:pThis paper addresses this gap, using a mixed‐methods approach to explore the influence of 33 different measures (including non‐structural factors) that could affect the number of hours girls spend using EdTech at home.</jats:p></jats:list-item> jats:list-itemjats:pFindings from a quantitative sample of girls (jats:italicn</jats:italic> = 544) and a qualitative sample of girls' caregivers (jats:italicn</jats:italic> = 58) highlighted the importance of non‐structural factors, especially caregiver permission.</jats:p></jats:list-item> jats:list-itemjats:pThe variable most strongly associated with girls' EdTech usage in our selected quantitative model concerned whether this was sanctioned by their caregivers.</jats:p></jats:list-item> jats:list-itemjats:pOur qualitative data suggested why caregiver permission to use EdTech might be withheld: caregivers emphasised perceived concerns about the risks and rigour of EdTech.</jats:p></jats:list-item> </jats:list> </jats:p>jats:pImplications for practice and/or policy<jats:list list-type="bullet"> jats:list-itemjats:pOur findings suggest the viability of policy interventions that provide EdTech guidance to caregivers.</jats:p></jats:list-item> jats:list-itemjats:pCaregivers uncertain about EdTech could be reassured of the appropriateness of verified initiatives, while those already convinced might be aided in their attempts to support EdTech learning.</jats:p></jats:list-item> jats:list-itemjats:pSuch guidance could provide a low‐cost means of further exploiting the benefits that household EdTech learning can provide.</jats:p></jats:list-item> </jats:list> </jats:p></jats:boxed-text> </jats:p></jats:sec>