In recent years, there has been a growing interest in chatbots that play counseling roles in the psychological health field. Previous studies have proposed counseling chatbots; however, they have not examined the anthropomorphic characteristics of agents in detail. In counseling situations, the characteristics of counselors and counselees affect the counseling performance. This study proposes a counseling chatbot to perform an intake interview and classifies the anthropomorphic characteristics of the chatbot into three dimensions—gender, personality, and visual interface cue—to examine these characteristics’ effects on user self-disclosure and companionship. Moreover, we examine the differences caused by user characteristics. No independent effect was observed for the gender of the chatbot or personality dimensions; however, a visual interface cue adversely affected the self-disclosure and companionship of the participants, and interaction effects were observed according to the combination of anthropomorphic characteristics. Furthermore, we found that the preferred chatbot differed according to gender. These results are significant and show that visual interface cues should be applied carefully while designing counseling chatbots. The anthropomorphism of the chatbot needs to be adjusted according to the user characteristics.

While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems.

While intelligence analysis has been a primary target domain for visual analytics system development, relatively little user and task analysis has been conducted within this area. Our research community’s understanding of the work processes and practices of intelligence analysts is not deep enough to adequately address their needs. Without a better understanding of the analysts and their problems, we cannot build visual analytics systems that integrate well with their work processes and truly provide benefit to them. In order to close this knowledge gap, we conducted a longitudinal, observational field study of intelligence analysts in training within the intelligence program at Mercyhurst College. We observed three teams of analysts, each working on an intelligence problem for a 10-week period. Based on the findings of the study, we describe and characterize processes and methods of intelligence analysis that we observed, make clarifications regarding the processes and practices, and suggest design implications for visual analytics systems for intelligence analysis.