Reinforcement Learning–Based Adaptive Interaction Product Design for Children’s Digital Health Intelligent Toys

Junfeng Zhu, Manjia Gao, Xiaocheng Song

Adaptive interaction design plays a critical role in improving user experience and health outcomes in children’s digital health intelligent toys. However, most existing toy interaction mechanisms rely on predefined rules or static strategies, which are insufficient to accommodate diverse behavioral patterns and dynamic engagement needs of children. To address this limitation, this paper proposes a reinforcement learning–based adaptive interaction product design framework for children’s digital health intelligent toys. In the proposed approach, user interaction states are modeled based on behavioral and engagement-related features, while toy responses are formulated as adaptive actions optimized through a reward function associated with interaction effectiveness and health-oriented objectives. A closed-loop learning mechanism is established to enable continuous policy optimization through real-time interaction feedback. On this basis, an adaptive product interaction architecture is designed to integrate perception, decision-making, and response modules within intelligent toys. Experimental evaluations conducted on representative interaction scenarios demonstrate that the proposed method achieves improved user engagement and interaction adaptability compared with conventional rule-based interaction designs. The results indicate that reinforcement learning provides an effective and scalable solution for adaptive interaction product design in children’s digital health intelligent toys.