Voice user interface (VUI) is widely used in developing intelligent products due to its low learning cost. However, most of such products do not consider the cognitive and language ability of elderly people, which leads to low interaction efficiency, poor user experience, and unfriendliness to them. Firstly, the paper analyzes the factors which influence the voice interaction behavior of elderly people: speech rate of elderly people, dialog task type, and feedback word count. And then, the voice interaction simulation experiment was designed based on the wizard of Oz testing method. Thirty subjects (M = 61.86 years old, SD = 7.16; 15 males and 15 females) were invited to interact with the prototype of a voice robot through three kinds of dialog tasks and six configurations of the feedback speech rate. Elderly people’s speech rates at which they speak to a person and to a voice robot, the feedback speech rates they expected for three dialog tasks were collected. The correlation between subjects’ speech rate and the expected feedback speech rate, the influence of dialog task type, and feedback word count on elderly people’s expected feedback speech rate were analyzed. The results show that elderly people speak to a voice robot with a lower speech rate than they speak to a person, and they expected the robot feedback speech rate to be lower than the rate they speak to the robot. There is a positive correlation between subjects’ speech rate and the expected speech rate, which implies that elderly people with faster speech rates expected a faster feedback speech rate. There is no significant difference between the elderly people’s expected speech rate for non-goal-oriented and goal-oriented dialog tasks. Meanwhile, a negative correlation between the feedback word count and the expected feedback speech rate is found. This study extends the knowledge boundaries of VUI design by investigating the influencing factors of voice interaction between elderly people and VUI. These results also provide practical implications for developing suitable VUI for elderly people, especially for regulating the feedback speech rate of VUI.

Waiting is an indispensable and inevitable part of man-machine voice interaction. The voice user interface (VUI) feedback mechanism is a key factor affecting voice interaction’s waiting experience. The feedback time of most available voice interfaces is fixed or decided by the processing time of hardware and software, which has not been designed and cannot offer users a good interaction experience. In this paper, the speech rate of user-machine voice interaction is collected through prototype experimentation. Besides, users’ time perception of different voice interfaces’ feedback time settings is studied based on time psychology theories. Moreover, users’ emotional changes are described after a specific feedback time with the distribution of two-dimension arousal-valence emotion space. Users’ time perception and subjective emotions are differently influenced by different VUI feedback times. The experimental results show that 750 ms is the optimal VUI feedback time point at which the best users’ subjective feelings and psychological experiences are reached, and the threshold limit time spent by users in waiting for the VUI feedback is 1,850 ms which will lead to user emotions with low levels of arousal and valence after being exceeded. Based on that, a linear regression model is proposed to define the optimal feedback time of VUI. The user experience VUI research results show that the calculated feedback time parameters can make users produce time perception in line with their expectations in interacting with voice interfaces.

Navigational voice interaction is an important factor affecting driving behavior and safety under complex road conditions. However, much of the research on navigational speech has focused on aspects such as commands and tone, and little on voice familiarity. To investigate the effects of navigation voice familiarity on driving stress and emotions, this study was conducted through simulated driving and controlled experiments. Driving stress was assessed by analyzing participants’ electrocardiogram (ECG) signals and subjective data obtained from a Driving Activity Load Index (DALI) questionnaire. Driving emotions were evaluated by participants’ facial expression data and the Self-Assessment Manikin (SAM), whereas the Post-study System Usability Questionnaire (PSSUQ) scores were collected as a complementary measure of both driving stress and emotions. We found that voice familiarity had a substantial impact on driving stress and emotions. Compared with using a familiar voice, an unfamiliar voice resulted in a 26.51% increase in participants’ ECG data and a 34.12% increase in DALI total scores. In addition, the use of an unfamiliar voice was associated with a 112.50% and 200% increase in the mean intensity value of anger and disgust among participants, respectively, as well as a decrease in their PSSUQ ratings. These findings indicate that the use of unfamiliar navigation voices in complex road conditions can exacerbate driving stress and negative emotions. This study provides valuable insights for navigational Voice User Interaction (VUI) design and ultimately contributing to driving safety.

Light can significantly influence users’ physiological and behavioural performance. However, how light breathing influences users’ mood regulation remains unknown. To fill this gap, this study conducted a 2-by-2 experiment (N = 20) with light breathing as the between-subject factor and light condition as the within-subject factor. Both physiological indicators and subjective indicators are measured to reflect mood regulation. The data were analysed using a generalised linear mixed model. The results showed that breathing lights are effective in regulating users’ moods. More specifically, breathing lights help users lower their electrodermal values and heart rates. Users did not report any significant difference in terms of subjective measures, which suggest that the influence of a breathing light happens unconsciously. Furthermore, this effect is significant for both cold and warm colour temperatures. Designers and engineers can use the research findings to manage user emotion when necessary.

Digital transmission of traditional cultural heritage faces challenges of insufficient user engagement and superficial dissemination. Using the Cizhou kiln as a case study, this research proposes the “Creation-as-Transmission” design paradigm, which transcends traditional unidirectional display models by transforming users from cultural consumers to cultural co-creators. This paradigm systematically integrates situated cognition theory and co-creation theory, achieving deep cultural learning and socialized transmission through AI collaborative creation. Empirical studies ( N = 200) demonstrate that compared to traditional methods, this paradigm significantly enhances cultural cognition (74% improvement) and the proportion of spontaneous sharing on social media (82.6% sharing rate). More importantly, this research provides the first quantification of causal mechanisms between creative behaviors and cultural cognition, establishing a predictive model explaining 73.1% of cognitive variance and offering mechanistic evidence for situated learning theory’s application in cultural heritage transmission. The research contributions have threefold value: theoretically, it expands HCI understanding of the relationship between “design behaviors and cognitive effects”; methodologically, it provides a transferable implementation framework for other intangible cultural heritage; practically, it opens new pathways for digital technologies to promote sustainable cultural transmission.