Children’s interaction with digital platforms requires safety mechanisms capable of responding to changing behavioral and contextual risks. This paper proposes an adaptive child-safety interface based on multimodal behavioral risk scoring, an adaptive UI policy engine, and human-in-the-loop supervision. Visual-content, sequential-behavior, text, and contextual risk signals are combined using a weighted fusion model and transformed into four risk levels that dynamically trigger interface actions. The proposed decision logic was implemented as a simulation-based computational prototype and evaluated using 2,000 synthetic interaction sessions representing normal use, accidental risky actions, repeated restricted-content access, and critical unsafe behavior. The prototype achieved an F1-score of 0.936 for high-risk event detection, a false positive rate of 0.006, and a mean detection latency of 1.16 s. The results demonstrate the operational consistency of the adaptive policy under controlled synthetic conditions. The study does not claim real-world validation of the underlying CNN, RNN, or NLP models; validation using ethically collected age-stratified behavioral data remains necessary.