Differences in User Perception of Artificial Intelligence-Driven Chatbots and Traditional Tools in Qualitative Data Analysis  
[PERSON]  
1 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (I.K. ); [EMAIL_ADDRESS] (S.B.) [PERSON]  
1 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (I.K. ); [EMAIL_ADDRESS] (S.B.) [PERSON]  
1 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (I.K. ); [EMAIL_ADDRESS] (S.B.) [PERSON]  
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[PERSON]  
1 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; [EMAIL_ADDRESS] (M.P. ); [EMAIL_ADDRESS] (I.K. ); [EMAIL_ADDRESS] (S.B.) Footnote 1: email: [EMAIL_ADDRESS],  
we were interested in finding an answer to the question of how, among many other factors, user experience (UX), usability, trust, affect, and mental load can be recognized as predictors of user perception when evaluating artificial intelligence-driven chatbots and traditional qualitative data analysis tools. Therefore, the strengths and weaknesses of chatbots compared to other digital technologies for the same tasks need to be carefully analyzed, including studies on the interaction between humans and computers. The field of human-computer interaction (HCI) has been thoroughly researched, but studies combining HCI and chatbots, although numerous, are still at an early stage. What hinders practical researchers is also the fact that the functions and capacities that can influence the UX of chatbots change daily. Usability criteria such as effectiveness, efficiency, and satisfaction, for example, have been used to determine how successfully users can learn and use chatbots to achieve their goals and how satisfied users are with their use [5, 6, 7, 8, 9, 10, 11, 12]. Comparative studies between tasks performed by chatbots and \"traditional digital tools\" are even rarer, necessitating additional research [13]. Even though in the last five years, the number of publications in the field of evaluating the usability and UX of chatbots has started to increase, the existing literature reveals only a very limited number of studies that specifically analyze the usability of chatbots and the UX when using this type of technology for qualitative data analysis. This highlights the need for further research in this area. Our study aims to (1) identify and adapt standard measurement instruments for evaluating chatbots in the context of qualitative data analysis, and (2) evaluate and compare the usability and UX of chatbots for qualitative analysis and determine their (dis)advantage over non-AI-based tools. Based on previous research findings [5, 7, 9, 10, 11, 12], our study aims to adapt and combine several different measurement instruments that will enable a more holistic analysis of the usability and UX of chatbots in the context of qualitative data analysis. At the same time, one of the goals of this study is to determine whether there are differences in usability and UX perceptions between chatbots and non-AI and natural language processing (NLP)-based QDA tools. This study may be regarded as piloting in HCI research by providing new insights into end users' perceptions about the usability and UX of chatbots used in qualitative research. Additionally, based on the study's results, validated measurement instruments provide the researchers with a tool for future research in this field. The primary goal of the research is to analyze users' perceptions of the usability and UX of AI-based and non-AI-based tools for qualitative data analysis. To accomplish this, the following research questions served as the foundation for our study design:  
1. Are there significant differences in the usability and UX of chatbots and non-AI tools in the qualitative data analysis process? 2.