Background Tuberculosis (TB) is a highly infectious disease. Negative perceptions and insufficient knowledge have made its eradication difficult. Recently, mobile health care interventions, such as an anti-TB chatbot developed by the research team, have emerged in support of TB eradication programs. However, before the anti-TB chatbot is deployed, it is important to understand the factors that predict its acceptance by the population. Objective This study aims to explore the acceptance of an anti-TB chatbot that provides information about the disease and its treatment to people vulnerable to TB in South Korea. Thus, we are investigating the factors that predict technology acceptance through qualitative research based on the interviews of patients with TB and homeless facility personnel. We are then verifying the extended Technology Acceptance Model (TAM) and predicting the factors associated with the acceptance of the chatbot. Methods In study 1, we conducted interviews with potential chatbot users to extract the factors that predict user acceptance and constructed a conceptual framework based on the TAM. In total, 16 interviews with patients with TB and one focus group interview with 10 experts on TB were conducted. In study 2, we conducted surveys of potential chatbot users to validate the extended TAM. Survey participants were recruited among late-stage patients in TB facilities and members of web-based communities sharing TB information. A total of 123 responses were collected. Results The results indicate that perceived ease of use and social influence were significantly predictive of perceived usefulness (P=.04 and P<.001, respectively). Perceived usefulness was predictive of the attitude toward the chatbot (P<.001), whereas perceived ease of use (P=.88) was not. Behavioral intention was positively predicted by attitude toward the chatbot and facilitating conditions (P<.001 and P=.03, respectively). The research model explained 55.4% of the variance in the use of anti-TB chatbots. The moderating effect of TB history was found in the relationship between attitude toward the chatbot and behavioral intention (P=.01) and between facilitating conditions and behavioral intention (P=.02). Conclusions This study can be used to inform future design of anti-TB chatbots and highlight the importance of services and the environment that empower people to use the technology.

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