## 6 Conclusions  
Evaluating the usability, user experience, emotional impacts, and workload of tools designed for qualitative data analysis is critical for understanding how these systems influence users' performance and satisfaction. Tools like ChatGPT, Gemini, and Taguette each present unique strengths and weaknesses in these areas, highlighting the importance of tailoring tools to users' needs.  
The findings from this study underscore that AI-based tools, particularly ChatGPT, consistently foster more positive user experiences, reduce negative emotional impacts, and lower cognitive workload compared to traditional tools like Taguette. ChatGPT's high usability score and strong hedonic quality distinguish it, whereas Gemini demonstrates balanced positive and negative emotions across tasks. Taguette, on the other hand, shows a higher mental workload and stronger negative emotions, suggesting areas for improvement, particularly for tasks requiring substantial cognitive effort.  
It is important to note that this study involved a limited group of respondents, primarily Master's students in informatics and data technologies, which may affect the generalizability of these findings. The conclusions presented here are therefore most applicable to this specific demographic and should not be extended to other user groups without further validation. For example, secondary school students, PhD candidates, industry representatives, or staff members may exhibit different usage patterns and perceptions when interacting with these tools.  
Nevertheless, these insights provide valuable guidance for the design and implementation of effective QDA tools. Several factors influence the perceived effectiveness of these technologies, including usability, cognitive effort, and emotional impact. This study highlights the value of leveraging AI technologies to simplify complex qualitative tasks, improve user satisfaction, and inform the development of tools that better align with diverse user needs. Future research should aim to include broader and more representative user groups, as well as explore varying contexts of use, to enhance the robustness and generalizability of findings in this evolving field.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15020631/s1](https://www.mdpi.com/article/10.3390/app15020631/s1)]([https://www.mdpi.com/article/10.3390/app15020631/s1](https://www.mdpi.com/article/10.3390/app15020631/s1)), Table S1: SEQ Measurement Instrument; Table S2: I-PANAS-SF Measurement Instrument; Table S3: NASA-TLX: Task Load Index Measurement; Table S4: UEQ-S Measurement Instrument; Table S5: SUS Measurement Instrument; Table S6: TOAST Measurement Instrument.  
**Author Contributions:** Conceptualization: B.S. and S.B.; methodology: B.S. and S.B.; formal analysis: B.S. and S.B.; investigation: B.S., S.B., M.P. and I.K.; data curation: B.S., S.B. and I.K.; writing--original draft preparation: B.S. and S.B.; writing--review and editing: B.S., S.B., A.S., M.P. and I.K.; visualization, B.S. and S.B., supervision: B.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and was approved by the Research Ethics Committee of the Faculty of Arts, University of Maribor (approval number: 038-33-199/2024/7/FF/UM, approved on 17 December 2024).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Acknowledgments:** The authors acknowledge the financial support from the Slovenian Research and Innovation Agency (research core funding No. P2-0057).  
**Conflicts of Interest:** The authors declare no conflicts of interest.