Music-streaming platforms rely on recommender systems to help listeners navigate millions of tracks, including a growing number of children using these platforms. However, most systems are optimized for adults, often resulting in recommendations that fail to reflect preferences or needs of children. While demographic features have been shown to improve performance in models focused on adults, their impact on a child-centric recommender system remains unexplored. This study investigates whether incorporating demographic features (age, gender, and country) and profiling features (exploratoryness, concentration, and replayness) improves the quality of music recommendations for children. Using a filtered subset of the LFM-2b dataset, we evaluate a baseline model based on implicit-feedback interactions against variants extended with different combinations of demographic and profiling features. Results show that demographic features led to a reduced accuracy across most models. In contrast, profiling features significantly increase top-K accuracy, with improvements up to 18%. These findings highlight the limitations of recommender systems tuned for adults when applied to children and emphasize the value of behavioral-aware modeling in the development of more effective child-centric music recommender systems.