With the development of artificial intelligence technology, voice-based intelligent systems (VISs), such as AI speakers and virtual assistants, are intervening in human life. VISs are emerging in a new way, called human–AI interaction, which is different from existing human–computer interaction. Using the Kansei engineering approach, we propose a method to evaluate user satisfaction during interaction between a VIS and a user-centered intelligent system. As a user satisfaction evaluation method, a VIS comprising four types of design parameters was developed. A total of 23 subjects were considered for interaction with the VIS, and user satisfaction was measured using Kansei words (KWs). The questionnaire scores collected through KWs were analyzed using exploratory factor analysis. ANOVA was used to analyze differences in emotion. On the “pleasurability” and “reliability” axes, it was confirmed that among the four design parameters, “sentence structure of the answer” and “number of trials to get the right answer for a question” affect the emotional satisfaction of users. Four satisfaction groups were derived according to the level of the design parameters. This study can be used as a reference for conducting an integrated emotional satisfaction assessment using emotional metrics such as biosignals and facial expressions.

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Innovative sociotechnical change is forthcoming because of autonomous driving; however, only a few studies have focused on the acceptance of this technology, which is not up to social expectation. In this study, we present and validate a research framework on the basis of the Kano model to identify the effective acceptance elements for autonomous driving technology. By collecting and analyzing the survey data of 187 people, it was confirmed that the elements of acceptance for autonomous driving technology can be classified according to the Kano attributes. This means that these acceptance elements should be resolved with priority in order to secure the acceptance. Legal policies and ethical guidelines are identified as top priorities for ensuring the acceptance of autonomous driving. Traffic congestion, situational awareness, malfunction prevention, and fatigue/stress relief must be addressed as utmost priorities. The framework and results from this study can be used to establish efficient strategies for developing autonomous driving technologies according to the user requirement levels.

The advancements in artificial intelligence technology have made changes in how people interact with systems. Unique features and user requirements of Human-AI Interactions (HAII) need to be identified with respect to those of Human-Computer interactions (HCI). This study proposes a way to find critical parameters of interaction design for enhancing user’s satisfaction when people interact with intelligent systems through voice user interfaces. We summarised distinguished user requirements for intelligent products identified from previous researches. Then match them with design parameters in terms of performance indexes that will make differences in the user’s satisfaction. The interaction scenario was set as users ask simple questions with their own voices to the system and the system answer to the questions with synthesized voices after it got to the answer by AI function. The critical performance indexes derived are the number of trials to get the right answer for a question, response time to get to the next interaction, sentence structures of the answer, and pace of the answer. An experimental setup is ready to evaluate user’s satisfaction among different levels of the above performance indexes by Wizard of Oz design method applied on a voice user interface we implement. We are going to validate the effects of performance indexes in HAII on the user’s satisfaction, which will be measured in terms of verbal and non-verbal measures.