While interactive technologies show promise for autistic children, current designs often overlook the heterogeneity of this population, offering standardized experiences that fail to account for individual profiles. We present WeJoy Mat, a multi-sensory embodied system designed to mediate parent-child co-play through a mirrored game paradigm. We conducted a mixed-methods study with 11 dyads, triangulating gameplay logs, behavioral rating, and qualitative interviews. Our analysis revealed that interaction outcomes were not driven by specific sensory modalities alone, but were moderated by the fit between these modes and children’s individual cognitive-behavioral phenotypes. Consequently, the system functioned as an active "third actor" within the interaction triad, where parents provided scaffolding while the mat redistributed agency and facilitated parallel synchrony. Our work advocates for a shift from rigid presets to flexible, parent-controlled tools that effectively move design away from correction to truly neuro-affirming environments that support the unique needs of each child.

Semi-structured interview is an important method in human-computer interaction research. However, traditional methods often rely on the experience and skills of the interviewer, limiting the quality and flexibility of interview outline. We propose a semi-structured interview system based on fine-tuned large language model (LLM) and reinforcement learning from human feedback (RLHF). Our system uses knowledge in the domain of human-computer interaction to fine-tune LLM and adopts the RLHF method based on multi-task learning and entropy regularization to dynamically adjust interview strategies. The system also collects multimodal data such as speech, video, and emotion to provide comprehensive support for subsequent analysis. Simulation experiment and pilot experiment show that compared to traditional human interview and other baseline methods, our system performs well in terms of interview relevance, personalization, engagement, and efficiency. Qualitative analysis further reveals the system's advantages in terms of conversational fluency, personalization, and efficiency. This innovative interview method is expected to play an important role in future user research, providing researchers with a more efficient and comprehensive data collection tool.

Bio-inspired design (BID), as an important method for product innovation, still has many challenges in knowledge retrieval efficiency, feature mapping and solution generation. We proposed a dialogical BID method based on large language model (LLM), aiming to enhance the efficiency and innovation of the design process through human-computer collaboration. Using the conceptual design of a flying car as a case study, we accomplished an innovative transformation from the biological features of a humpback whale to a product form through multiple rounds of dialog with LLM. Experimental evaluation showed that the method was outstanding in terms of novelty and fashion and symbolization. The study confirms the effectiveness of LLM in BID and provides new methodological ideas for product innovation.

To tackle the challenge of achieving more reasonably calculation of evaluation indicator weights and a more scientifically rigorous scoring system for ranking in product design evaluation, a novel evaluation model that leverages improved CRITIC (Criteria Improved Criteria Importance) and CC-TOPSIS (Comprehensive Cloud-Technique for Order Preference by Similarity to Ideal Solution) is proposed. Firstly, the process commences with the collection of qualitative product evaluations from respondents through questionnaires, presented in the form of fuzzy confidence intervals. Subsequently, these evaluations are transformed into generalized trapezoidal fuzzy numbers, and their center of gravity values are calculated. Secondly, after the weight for each evaluation indicator is calculated employing an enhanced CRITIC approach, a weight cloud model utilizing the backward cloud methodology is constructed. Subsequently, the scoring cloud model is generated as a backward cloud, followed by the application of an enhanced synthesis operator to amalgamate the weight cloud model with the scoring cloud model, culminating in the formation of a comprehensive cloud model. Finally, the product design schemes are ranked using the Cloud-TOPSIS method, and a cloud chart analysis provides insights into optimization directions. The utilization of this methodology in assessing automotive styling design serves as a compelling showcase of its practicality and efficiency, in addition to, the method's resilience is further bolstered through a sensitivity analysis of the evaluation indicators. This method adeptly computes the weights of evaluation indicators, while also addressing the inherent variability in weight values and incorporates the benefits of Cloud Model and TOPSIS in the evaluation of product design alternatives, ultimately elevating the rationality and the scientific underpinnings of the product design evaluation process.