Gender stereotypes form early in the child’s development and are carried over throughout adolescence into adulthood, leaving long-lasting effects which may impact activity and career choices, as well as academic performance. Books, in particular, can have considerable influence, as their characters serve to shape role models of femininity and masculinity for young children. Thus, gender under- and misrepresentation in children’s textual literature can contribute to the internalization and reinforcement of negative stereotypes. To address this issue, we aim to identify and measure relevant dimensions of gender bias in children’s books with the aid of both qualitative and quantitative techniques: systematic literature review across disciplines, synthesis and (expert) validation on the one hand and state-of-the-art NLP methods on the other. By exploiting such an integrated research framework, we believe that we can automate the detection of potentially biased text while enhancing the interpretability and transparency of the results.