## 2 Backgrounds
### Research on Chatbot Usability and User Experience  
In the existing literature, a steep growth is observed in the number of publications evaluating the usability and UX of chatbot solutions (see Table 1). The first studies examining the value of contemporary chatbot solutions were published in 2019 and 2020. However, it is clear that, since 2021, interest in chatbot UX and usability research increased significantly. The body of literature currently provides a rich collection of scientific works investigating various aspects of usability, UX, and factors that significantly influence the acceptance and use of chatbot solutions in different domains or fields. As can be seen in Table 1, a considerable part of the study related to the UX and usability of chatbots has been carried out in the health domain (e.g., [47; 48; 49; 50; 51; 52; 53]) and related areas such as mental health (e.g., [10; 54; 55; 56; 57]), digital health [58], and well-being [59]. Several chatbot UX and usability studies have also been conducted in the context of education (e.g., [14; 51; 60; 61]) and higher education [62]. We can also find studies that evaluated chatbot solutions in other domains such as e-retailing [63], customer support services [8], customer relationship management [11], tourism [13], research in pharmacy [64], and others. [PERSON], for example, evaluated the usability and users' satisfaction when using ChatGPT for text generation [65]. \begin{table}
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Existing research on chatbot usability and UX covers a broad spectrum of applications, with studies examining factors like task completion, user satisfaction, effectiveness, and specific usability metrics such as the System Usability Scale (SUS) (e.g., [9; 14; 49; 51; 54; 56; 58; 60; 61; 62; 64; 65; 66]), the Chatbot Usability Questionnaire (CUQ) (e.g., [48; 50; 53; 56; 64]), the User Experience Questionnaire (UEQ) (e.g., [47; 55; 57; 60; 64]). The Usability Metric for User Experience LITE (UMUX-LITE) [67] offers a quick and reliable measure of usability with a strong correlation to the SUS. [PERSON] et al. validated the UMUX-LITE tool extensively in the context of chatbot usability, demonstrating its applicability and robustness for evaluating conversational systems and highlighting its advantages as a streamlined alternative to traditional usability scales [11; 12]. We found only one study in which the authors built a measurement instrument based on the International Positive and Negative Affect Schedule Short-Form (I-PANAS-SF) [10]. Studies evaluating factors that affect chatbot UX and usability also include other metrics, such as attractiveness [55], cognitive load [13], customer satisfaction [63], ease of completing tasks [59], ease of use [9; 52], learnability [62], perceived autonomy [13], perceived competence [13], satisfaction with operation [13], responsiveness [63], satisfaction with the system [13], satisfaction [57], system reliability [62], task performance [59], utility [9; 52; 62], utility errors [59], and others. Existing research that used the SUS instrument for usability evaluation provided diverse results. Although not all articles reported final SUS scores (on a 0 to 100 scale), the analysis of SUS scores published in the existing literature reveals that the usability of chatbots varies significantly between the lowest score of 60.03 (for the chatbot myHardware [61] that is used in education) and the highest score of 88.2 (for the chatbot iHelpr used in mental health). For example, the WeightMentor chatbot for weight loss maintenance showed high ease of task completion and positive usability scores despite some usability errors [59]. Similarly, the mental health chatbot iHelpr received a high SUS score of 88.2, indicating high usability compared to industry standards [54]. In a healthcare setting, the chatbot-based questionnaire at a preoperative clinic was preferred over traditional computer questionnaires, even though completion times were similar [47].