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Stating that one trusts a system is markedly different from demonstrating that trust. To investigate trust in automation, we introduce the trust fall: a two-stage behavioral test of trust. In the trust fall paradigm, first the one learns the capabilities of the system, and in the second phase, the ‘fall,’ one’s choices demonstrate trust or distrust. Our first studies using this method suggest the value of measuring behaviors that demonstrate trust, compared with self-reports of one’s trust. Designing interfaces that encourage appropriate trust in automation will be critical for the safe and successful deployment of partially automated vehicles, and this will rely on a solid understanding of whether these interfaces actually inspire trust and encourage supervision.

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Background Approximately 60%-80% of the primary care visits have a psychological stress component, but only 3% of patients receive stress management advice during these visits. Given recent advances in natural language processing, there is renewed interest in mental health chatbots. Conversational agents that can understand a user’s problems and deliver advice that mitigates the effects of daily stress could be an effective public health tool. However, such systems are complex to build and costly to develop. Objective To address these challenges, our aim is to develop and evaluate a fully automated mobile suite of shallow chatbots—we call them Popbots—that may serve as a new species of chatbots and further complement human assistance in an ecosystem of stress management support. Methods After conducting an exploratory Wizard of Oz study (N=14) to evaluate the feasibility of a suite of multiple chatbots, we conducted a web-based study (N=47) to evaluate the implementation of our prototype. Each participant was randomly assigned to a different chatbot designed on the basis of a proven cognitive or behavioral intervention method. To measure the effectiveness of the chatbots, the participants’ stress levels were determined using self-reported psychometric evaluations (eg, web-based daily surveys and Patient Health Questionnaire-4). The participants in these studies were recruited through email and enrolled on the web, and some of them participated in follow-up interviews that were conducted in person or on the web (as necessary). Results Of the 47 participants, 31 (66%) completed the main study. The findings suggest that the users viewed the conversations with our chatbots as helpful or at least neutral and came away with increasingly positive sentiment toward the use of chatbots for proactive stress management. Moreover, those users who used the system more often (ie, they had more than or equal to the median number of conversations) noted a decrease in depression symptoms compared with those who used the system less often based on a Wilcoxon signed-rank test (W=91.50; Z=−2.54; P=.01; r=0.47). The follow-up interviews with a subset of the participants indicated that half of the common daily stressors could be discussed with chatbots, potentially reducing the burden on human coping resources. Conclusions Our work suggests that suites of shallow chatbots may offer benefits for both users and designers. As a result, this study’s contributions include the design and evaluation of a novel suite of shallow chatbots for daily stress management, a summary of benefits and challenges associated with random delivery of multiple conversational interventions, and design guidelines and directions for future research into similar systems, including authoring chatbot systems and artificial intelligence–enabled recommendation algorithms.