Marginalized communities often face interconnected barriers that undermine well-being, yet design methods rarely explicitly account for how disadvantages compound or how strengths can reinforce each other. Building on Capability Sensitive Design (CSD), this research extends the framework to address corrosive disadvantages—barriers that undermine multiple capabilities—and fertile functionings—capabilities that positively reinforce others. We applied this extended framework in a participatory study with newcomers to Canada. Using capability hierarchy mapping, co-design workshop, and field study, we identified key capability gaps and their interconnectedness, surfaced community knowledge, and translated values into actionable design requirements. Our findings show that explicitly mapping advantages and disadvantages enables more targeted, contextually grounded interventions. We conclude with methodological guidance for applying this approach to other marginalized contexts in HCI, where designing for equity requires accounting for how capabilities interact.

With the rapid advancement of Brain-Computer Interface (BCI) technology, real-time control of external devices via electroencephalogram (EEG) signals has become feasible, offering new avenues for building more natural and efficient human-computer interaction systems. To address these issues, this paper proposes a Rule-based Motor Imagery Interaction Model (Rule-MII Model) that combines rule matching with multifeature fusion. By leveraging discrete wavelet transforms, timefrequency domain analysis, and symbolic pattern mining, the model extracts multidimensional EEG features and constructs a standard rule set for recognizing users' motor intentions and emotional states. The proposed model achieves classification accuracies of 91.0 % and 92.7 % on the public BCI2a and DEAP datasets for motor imagery and emotion recognition tasks, respectively. Experimental results demonstrate that the proposed method exhibits strong accuracy and responsiveness in multimodal EEG signal modeling and mixed-reality-based human-robot interaction control, providing a promising solution for immersive and context-aware human-robot collaboration.

The robot remote control architecture aims to reduce the computing load of the robot and improve the robot's task response speed. To address the robot's limited computational resources, this paper proposes a remote control architecture that migrates complex computations to a remote Linux server. Tasks such as face recognition, person tracking, Q&A, and product information recognition are processed remotely, and the robot executes commands received from the Linux server, thereby reducing its local computational load. At the same time, the Feature Coefficients Selection-based Decision Tree (FCS-DT) Classification Method is proposed for deployment on the remote Linux server. This approach enables real-time monitoring and optimization of system performance bottlenecks, further enhancing the efficiency of robot task processing, and in the later experiments, FCS- DT shows high accuracy and recognition speed, and the architecture is highly efficient for the computation of complex tasks of robots. Therefore, this architecture has practical value for improving the processing efficiency of robot complex tasks.

As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach – majority voting – to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR – by approximately 9% and 31%, respectively – compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists’ navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists’ domain knowledge, we designed NaviPath — a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.

Creativity Support Tools (CSTs) aid in the efficient and effective composition of creative content, such as picture books. However, many existing CSTs only allow for mono-modal creation, whereas previous studies have become theoretically and technically mature to support multi-modal innovative creations. To overcome this limitation, we introduce XCreation, a novel CST that leverages generative AI to support cross-modal storybook creation. Nevertheless, directly deploying AI models to CSTs can still be problematic as they are mostly black-box architectures that are not comprehensible to human users. Therefore, we integrate an interpretable entity-relation graph to intuitively represent picture elements and their relations, improving the usability of the underlying generative structures. Our between-subject user study demonstrates that XCreation supports continuous plot creation with increased creativity, controllability, usability, and interpretability. XCreation is applicable to various scenarios, including interactive storytelling and picture book creation, thanks to its multimodal nature.

Recent developments in AI have provided assisting tools to support pathologists’ diagnoses. However, it remains challenging to incorporate such tools into pathologists’ practice; one main concern is AI’s insufficient workflow integration with medical decisions. We observed pathologists’ examination and discovered that the main hindering factor to integrate AI is its incompatibility with pathologists’ workflow. To bridge the gap between pathologists and AI, we developed a human-AI collaborative diagnosis tool— xPath —that shares a similar examination process to that of pathologists, which can improve AI’s integration into their routine examination. The viability of xPath is confirmed by a technical evaluation and work sessions with 12 medical professionals in pathology. This work identifies and addresses the challenge of incorporating AI models into pathology, which can offer first-hand knowledge about how HCI researchers can work with medical professionals side-by-side to bring technological advances to medical tasks towards practical applications.