A Human–AI Collaborative Design Methodology for an Adolescent Smartphone Addiction Management Device: Integrating LLM-AHP, SD-LLM, and Manual Refinement

Mitigating smartphone addiction among adolescents remains a significant challenge. Existing anti-addiction products exhibit critical limitations, including vulnerability to circumvention, over-reliance on user self-discipline, and insufficient coercive control. To address these issues, this study proposes a novel product design methodology integrating LLM-AHP (Large Language Model-Analytic Hierarchy Process), SD-LLM (Stable Diffusion-Large Language Model), and manual expert refinement across four iterative stages. The LLM-AHP analysis identified “coercive management independent of self-discipline” and “minimization of human intervention and family conflicts” as the most critical requirements. The final hardware-based device was developed with several key features, including a non-removable case, non-standard internal screws, a triple-verified contact detection device, an emergency call area, and a roller shutter. The workflow of “LLM-AHP → SD-LLM → Manual Refinement” facilitates precise requirement identification, design evaluation, and scheme generation, offering a reusable design framework for other hardware-based children’s products.