While artificial intelligence (AI) is being increasingly utilized in the design of museum cultural and creative products (MCCPs), limited research has explored consumer satisfaction with these AI-generated designs. This study quantitatively examined the impact of AI-generated MCCP design on consumer satisfaction and proposed strategies for improvement. A comprehensive evaluation system consisting of fourteen factor indicators across four dimensions was constructed through literature research, user interviews, and expert suggestions. On this basis, a survey with 297 consumers was then conducted using AI-generated cultural and creative products from the Dunhuang Museum. Additionally, the Importance–Performance Analysis (IPA) method was employed to analyze the importance of various factors in these AI-generated designs and their impacts on consumer satisfaction. The findings revealed that, while consumers expressed high satisfaction in terms of product functionality and creative attraction, improvements are needed in cultural expression and user experience. It is recommended that creating a multimodal museum database, developing structured prompt card models, and building an MCCP design platform with full-process AI participation would help to increase consumer satisfaction. This study can provide theoretical and practical references for the intelligent development of MCCPs’ design and promote the sustainable development of cultural heritage.

While generative artificial intelligence (Gen AI) is accelerating digital transformation and innovation in corporate product design (CPD), limited research has explored how designers adopt this technology. This study aims to identify the key factors and causal configurations that influence designers’ intentions to adopt Gen AI in CPD. This study involved 327 in-service designers as participants, employed semi-structured interviews and a questionnaire to collect data, and applied the grounded theory and fsQCA to analyze the data. The findings indicate the following: (1) Personal innovativeness, AI technological anxiety, perceived usefulness, task–technology fit, perceived risk, social influence, and organizational support are the key factors influencing designers’ adoption of Gen AI. (2) None of these factors constitute a necessary condition for designers to adopt Gen AI. (3) High adoption intention results from the interaction of multiple factors, which can be categorized into three driving logics: “task demand-driven”, “organizational environment-driven”, and “individual characteristics-driven”. It is recommended that corporate managers establish an AI training framework, foster a supportive organizational environment, and implement tailored strategies to facilitate the integration of new technologies. This study clarifies the factors influencing designers’ adoption of Gen AI in CPD and provides a framework for companies to effectively integrate AI systems into product design.

This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, and loss of motor coordination during rehabilitation. To achieve this, it proposes an innovative design method for a lower limb rehabilitation training system based on Bayesian networks and parallel mechanisms. A Bayesian network model is constructed based on expert knowledge and structural mechanics analysis, considering key factors such as rehabilitation scenarios, motion trajectory deviations, and rehabilitation goals. By utilizing the motion characteristics of parallel mechanisms, we designed a rehabilitation training device that supports multidimensional gait correction. A three-dimensional digital model is developed, and multi-posture ergonomic simulations are conducted. The study focuses on quantitatively assessing the kinematic characteristics of the hip, knee, and ankle joints while wearing the device, establishing a comprehensive evaluation system that includes range of motion (ROM), dynamic load, and optimization matching of motion trajectories. Kinematic analysis verifies that the structural design of the device is reasonable, aiding in improving patients’ gait, enhancing strength, and restoring flexibility. The Bayesian network model achieves personalized rehabilitation goal optimization through dynamic probability updates. The design of parallel mechanisms significantly expands the range of joint motion, such as enhancing hip sagittal plane mobility and reducing dynamic load, thereby validating the notable optimization effect of parallel mechanisms on gait rehabilitation.