## 2 Backgrounds
### Research on Chatbot Trust, Task Difficulty, Emotional Affect Evaluation and Mental Workload  
User trust measurement in intelligent chatbots has already been observed in prior related work due to its central role in chatbot adoption and interaction outcomes through a single, universally accepted method for measuring trust has not yet been established. A study that included 607 respondents and their adoption of ChatGPT recognized trust as a critical component of chatbot adoption. It also highlighted the challenge of excessive user trust in chatbot responses in the health care domain [68]. Trust has been measured with various constructs in the existing body of research. A recent study conducted by [PERSON] and [PERSON] [69] focused on analyzing perceived trust and transparency in ChatGPT. The results indicated that predictability acts as a bridge between how transparent the AI system is and the trust users develop in it. Transparency (via accuracy and clarity) has been observed to positively influence trust (by reducing distrust in users). A meta study on trust in the general AI domain has confirmed a lack of research observing trust in AI systems. The study identified characteristics and abilities as factors in human trust and performance and attributes as factors in AI systems while also mentioning contextual factors, where team and task impact were recognized [70]. A previously mentioned study on mental health and the well-being chatbot [56] utilized a simpler measurement of trust score, a single question on a five-point Likert scale for each task (where 1 = very low level of trust, 5 = very high level of trust). All five responses were then summed for each user. Task difficulty is a crucial metric for understanding the impact of different tools on the perception of a task's execution difficulty. A previous study on healthcare chatbots [59] has used Single Ease Question as a method for gathering data on users' prediction of the task difficulty (\"On a scale of 1 to 7, how easy do you think it will be to complete this task?\") and for measuring it post-hoc (\"On a scale of 1 to 7, how easy was it to complete this task?\"). Use of ChatGPT in asynchronous video interviews showed that users perceived job interview task difficulty as lower on a seven-scale Likert scale when they used verbatim ChatGPT answers (\(\overline{x}=2.68,Std=1.32\)) or when they personalized ChatGPT responses (\(\overline{x}=3.09,Std=1.21\)), compared to the group that had no access to it (\(\overline{x}=4.08,Std=1.43\)). The measurement of task difficulty is not limited to perceived difficulty. A more objective approach to measuring difficulty based on the percentage of users that have successfully completed the task has also been reported [56]. Emotional Affect Evaluation examines users' emotional responses and affective experiences during interactions with AI chatbots. A recent study compared responses in evoked sentiment in users when presented with ChatGPT-generated narratives and real tweets. The study observed 44 narratives about birth, death, hiring, and firing. The comparison of emotional affect was conducted using a full PANAS questionnaire. Narratives generated with ChatGPT reached higher sentimental values (in 72% of the observed cases) than tweets on the eleven PANAS groups. Sentiment levels of the content, generated with ChatGPT were statistically indiscernible from tweets only in about 9% of the cases [71]. Emotional affect can also be probed in semi-structured interviews, as shown in a study on users' emotional reactions when using ChatGPT for the task of generating academic feedback on students' (own) research proposals. It indicated two main emotional reactions: satisfaction, which was mostly related to tools paraphrasing and translation abilities, while dissatisfaction was observed with the received suggestions on mechanics [72]. Mental Workload assesses cognitive demands placed on users during interactions with AI chatbots.