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Emoji suggestion systems based on typed text have been proposed to encourage emoji usage and enrich text messaging; however, such systems’ actual effects on the chat experience are unknown. We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and compared these in two different studies. To investigate the effect of emoji suggestion in online conversations, we conducted a laboratory text-messaging study with 24 participants and a 15-day longitudinal field deployment with 18 participants. We found that participants picked more semantic suggestions than lexical suggestions and perceived the semantic suggestions as more relevant to the message content. Our subjective data showed that although the suggestion mechanism did not affect the chatting experience significantly, different mechanisms could change the composing behavior of the users and facilitate their emoji-searching needs in different ways.

Current text correction processes on mobile touch devices are laborious: users either extensively use backspace, or navigate the cursor to the error position, make a correction, and navigate back, usually by employing multiple taps or drags over small targets. In this chapter, we present two techniques, Type, Then Correct and JustCorrect, that utilize the power of artificial intelligence to improve the text correction experience on mobile devices. All of the techniques skip error-deletion and cursor-positioning procedures, and instead allow the user to type the correction first, and then apply that correction to a previously committed error. We evaluated these techniques in and the results show that correction with the new techniques was faster than de facto cursor and backspace-based correction.

We compared three common usability testing methods—Concurrent Think-Aloud, Retrospective Think-Aloud and Co-discovery—with frail older adults. We found that Co-discovery is the most effective method for this group. Additionally, we interviewed Human-Computer Interaction experts who work with older adults. These experts discussed, for instance, the importance of leveraging usability tests to enhance participant motivation to engage with technology. We consolidated our findings from the usability studies with older adults and from interviews with experts to create a set of recommendations for performing usability tests with frail older adults. One of the core recommendations is to enhance participants’ sense of autonomy and self-confidence during usability tests.

Data not suitable for classic parametric statistical analyses arise frequently in human–computer interaction studies. Various nonparametric statistical procedures are appropriate and advantageous when used properly. This chapter organizes and illustrates multiple nonparametric procedures, contrasting them with their parametric counterparts. Guidance is given for when to use nonparametric analyses and how to interpret and report their results.