Human-centered artificial intelligence (HCAI) is a design philosophy that prioritizes humans in the design, development, deployment, and use of AI systems, aiming to maximize AI’s benefits while mitigating its negative impacts. Despite its growing prominence in literature, the lack of methodological guidance for its implementation poses challenges to HCAI practice. To address this gap, this article proposes a comprehensive HCAI methodological framework (HCAI-MF) comprising five key components: HCAI requirement hierarchy, approach and method taxonomy, process, interdisciplinary collaboration approach, and multilevel design paradigms. A case study demonstrates HCAI-MF’s practical implications, while the article also analyzes implementation challenges. Actionable recommendations and a “three-layer” HCAI implementation strategy are provided to address these challenges and guide future evolution of HCAI-MF. HCAI-MF is presented as a systematic and executable methodology capable of overcoming current gaps, enabling effective design, development, deployment, and use of AI systems, and advancing HCAI practice.

The study addressed the working memory capacity (WMC) of gesture-command associations in gestural interaction and investigated the impact of compatibility between users’ mental models and the predefined gesture-command associations on WMC. Gestural interaction is a popular representative of natural interactions. Although gestural interaction intends to be natural, it has been criticized for not being so. One of the critical problems lies in learning and memorizing. WMC is a pivotal bottleneck that underlies learning and memorizing gesture-command associations, yet it remains unknown. Two standardized paradigms were used to estimate the WMC of gesture-command associations: change-detection task in Experiment 1, and span task in Experiment 2. Besides, we further examined the impact of compatibility on WMC. We found that two gesture-command associations can be retained in working memory under low-compatibility conditions, while three to five associations can be retained under high-compatibility conditions. The result implies that WMC of gesture-command associations is highly limited, while this cognitive limitation could be reduced by promoting the compatibility between users’ mental model and the predefined gesture-command associations. Designers for gestural interactions may require users to memorize two to five gesture-command associations, considering different application scenarios.

Keeping an appropriate level of trust in automated driving (AD) is critical to safe driving. Although ample studies have investigated factors affecting trust in AD, few studies have investigated whether the personality of drivers influences the trust in AD system. Considering that trust measured at a given point in time lies on a continuum between dispositional and history-based trust, the current research investigated the relationship between driver’s personality and dispositional as well as history-based trust. We revealed that personality affected the two types of trust in different ways: A significant negative correlation emerged between Neuroticism and dispositional trust of AD (Study 1), whereas a significant negative correlation between Openness and history-based trust was found when participants interacted with the AD system (Study 2). These results suggest that drivers’ personality has an impact on the trust in AD, which is further modulated by the experience of driver’s interaction with the AD system.

While AI has benefited humans, it may also harm humans if not appropriately developed. The priority of current HCI work should focus on transiting from conventional human interaction with non-AI computing systems to interaction with AI systems. We conducted a high-level literature review and a holistic analysis of current work in developing AI systems from an HCI perspective. Our review and analysis highlight the new changes introduced by AI technology and the new challenges that HCI professionals face when applying the human-centered AI (HCAI) approach in the development of AI systems. We also identified seven main issues in human interaction with AI systems, which HCI professionals did not encounter when developing non-AI computing systems. To further enable the implementation of the HCAI approach, we identified new HCI opportunities tied to specific HCAI-driven design goals to guide HCI professionals addressing these new issues. Finally, our assessment of current HCI methods shows the limitations of these methods in support of developing HCAI systems. We propose the alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI approach to the development of AI systems. We also offer strategic recommendation for HCI professionals to effectively influence the development of AI systems with the HCAI approach, eventually developing HCAI systems.

Mid-air gesture interaction has become one of the most promising human–computer interaction manners. However, the principles of designing pie menus for mid-air gesture interaction are largely lacking. Currently, no study has addressed the breadth and depth of the pie menu for mid-air gesture interaction. Here, in two experiments, we found that the breadth and depth of a pie menu had distinct influences on the operation performance in mid-air gestural interaction: breadth affects both the accuracy and reaction time of the operation, while depth only affects reaction time. Overall, when operation accuracy is the main concern, the breadth of the pie menu will be the key factor, and it should not exceed eight items (Experiments 1 and 2). However, when the operation also emphasizes efficiency, the depth of the pie menu should be considered: fewer layers lead to faster responses, with two layers yielding the best performance (Experiment 2).

Redirected walking allows users to walk naturally through a large immersive virtual environment while the physical space is limited. Previous studies have analyzed human sensitivity to redirected walking in a horizontal direction, but users also need to walk on slopes to change their height. In this work, we expand the vertical movement space by positioning users on virtual paths with slopes that are different from those of real paths. We conduct psychological experiments to explore human sensitivity to slope gains that describe the discrepancies between the slopes of paths in virtual and real environments. The investigation shows that humans can walk on virtual slopes that are higher or lower than the real position without detecting the slopes and establishes corresponding detection thresholds.

Most gestural interaction studies on gesture elicitation have focused on hand gestures, and few have considered the involvement of other body parts. Moreover, most of the relevant studies used the frequency of the proposed gesture as the main index, and the participants were not familiar with the design space. In this study, we developed a gesture set that includes hand and non-hand gestures by combining the indices of gesture frequency, subjective ratings, and physiological risk ratings. We first collected candidate gestures in Experiment 1 through a user-defined method by requiring participants to perform gestures of their choice for 15 most commonly used commands, without any body part limitations. In Experiment 2, a new group of participants evaluated the representative gestures obtained in Experiment 1. We finally obtained a gesture set that included gestures made with the hands and other body parts. Three user characteristics were exhibited in this set: a preference for one-handed movements, a preference for gestures with social meaning, and a preference for dynamic gestures over static gestures.