As artificial intelligence (AI) became a part of daily life, it has become important to determine user competence in using AI technology. Here, we propose the concept of AI literacy and develop a quantitative scale for obtaining accurate data regarding the AI literacy of ordinary users. We first identified the primary core constructs of AI literacy, including awareness, use, evaluation, and ethics. Next, we generated 65 items to capture these four constructs; only 31 items were retained after a three-step content validation process. Then, we conducted a survey, and collected two samples of data. By reducing the number of items using the first sample and performing reliability and validity tests on the second sample, we obtained a 12-item instrument for the quantitative measurement of AI literacy. The results confirmed that the proposed four-construct model is an adequate representation of AI literacy. Further, AI literacy is significantly related to digital literacy, attitude towards robots, and users’ daily usage of AI. This study will not only aid researchers in understanding how user competence in using AI technology affects human–AI interactions but will also help designers develop AI applications that are aligned with the AI literacy levels of the target users.

Online game addiction is a new mental disorder. This disorder is difficult to describe because of its comprehensive nature. Many online game players have problems controlling their playing time. They cannot stop playing a game that they enjoy. This research surveyed the past literature on "flow" and time disorder theory. A time distortion experiment was conducted. This research invited 64 children, teenagers, and young adults to investigate player skill and playing time effects on online game break-off. The playing experience and degree of time distortion were measured and analyzed. The results showed that both novice and expert online game players were subject to time distortion. The participants had difficulty breaking off from the game without intrusion by others in the real world. This research also suggests eight questions for self-evaluation for online game addiction.

This article presents a comprehensive literature review to investigate whether and why older adults accept handheld computers and how to design elderly-friendly handheld computers. Findings about acceptance, input devices, menu and functions, and output devices are summarized. First, older adults were under social pressure to use mobile phones, but they had low acceptance of advanced functions. Also, they had a different way to judge acceptance factors from younger adults. Second, older adults preferred the physical keyboard to the on-screen keyboard for text entry, whereas they preferred tapping the touch pad to the joystick and buttons for pointing tasks. Third, older adults had shallower mental representation of the mobile phone menus than younger adults. Navigation aids providing contextual information and large cognitive preview per screen could help them. Finally, recommended size and spacing for text and icons are presented.

After adopting mobile phones, most older adults use them only for calling and SMS. The purpose of this study is to extend their usage of mobile phones to new functions. To understand older adults’ requirements of mobile phones, a questionnaire was constructed and 351 Chinese older adults were recruited to complete the questionnaires. Data collected through the questionnaires were analysed using explorative factor analysis. The results revealed that older adults’ requirements were composed of 10 factors: Find a Specific Function, Awareness and Attractiveness, Readability, Personal Concern, Soft Keys and Multi-tap, Hardware Capacity, Touch Screen, Concern of Learning, Connectivity, and Social Influence. Then, from the above 10 factors, the 6 most important factors were revealed using multiple linear regression analysis. The results imply that accepting new functions is different from accepting a product. Readability and Find a Specific Function, which are critical for older adults’ acceptance of feature phones, are not determinants of their acceptance of new functions in smart phones.