## 5 Discussion
Comparing the results of the mental workload for the first task (classification of the survey responses) with other classification tasks from prior literature (\(min=8.00,25\%=30.15,50\%=46.00,75\%=51.20,max=84.30\), per [86]), the mental workload measured with the use of AI-tools would be positioned in the lowest scoring 25% of the related studies, while the mental load of the task with the use of the non-AI tool would be positioned in the highest 25% of the related studies. Comparing the results of the second task (sentiment analysis of responses) with other results reported for cognitive tasks by [PERSON] [86] (\(min=13.08,25\%=38.00,50\%=46.00,75\%=54.66,max=64.90\)), the mental load measured with the use of AI-tools would again be positioned in the lowest scoring 25% of the studies, while the mental load with the use of the non-AI tool would be positioned slightly above the highest 50% of the studies. The results of NASA-RTLX indicate that the use of AI tools for UX evaluation tasks lowers the cognitive workload on the evaluators compared to the use of non-AI tools. Analysis of NASA-TLX subscales indicated that AI tools (ChatGPT and Gemini) generally reduce mental, physical, and temporal demands compared to the non-AI tool (Taguette). Users reported lower frustration and lower demand for effort with AI tools. However, users' subjective performance satisfaction can vary, with Taguette reaching the highest mean performance satisfaction in both analyzed tasks. The comparison of effort measurements obtained with NASA-RTLX and SEQ revealed a strong, positive correlation (\(\tau_{b}=0.493\), \(p<0.001\)). Presented results are complementary to those reported by [PERSON] and [PERSON] [77], though the disparity could be attributed to the difference in task complexity (as reported in Section 4.6, the NASA-RTLX results indicate the tasks in our research were positioned in the highest 25% of tasks in similar research, based on the required mental load). Analyzing emotional responses reveals distinct patterns in user experiences across tasks and tools. Positive emotion scores were consistently high, with ChatGPT performing best, particularly in Task 1 (\(M=15.77\), Table 13), driven by high determination (\(M=3.73\), Table 11). However, a slight decline in positive emotion for ChatGPT in Task 2 (\(M=14.99\)) may suggest task-specific challenges. Taguette showed relatively strong positive emotions (\(M=15.8\)) but lacked consistency, while Gemini demonstrated balanced scores (\(M=15.34\) and \(M=15.21\)). Negative emotions varied significantly, with ChatGPT producing the lowest scores (\(M=6.83\)), indicating a supportive user experience (Table 13). In contrast, Taguette triggered higher negative emotions (\(M=8.59\) for Task 1), with users reporting increased nervousness (\(M=2.12\)) and upset (\(M=1.94\)). Gemini's scores were moderate (\(M=7.17\) for Task 1), lying between Taguette and ChatGPT. Statistical tests confirmed these trends, showing significant differences in negative emotions across tools (\(H=27.06\), \(p<0.001\), Table 13), while positive emotions showed no significant variation (\(H=0.08\), \(p=0.96\)). Overall, ChatGPT's combination of high positive and low negative emotions positions it as the most user-friendly tool, particularly for tasks requiring engagement and focus. These findings highlight the importance of designing tools that minimize negative emotional impacts while fostering positive user experiences. The study's findings provide several significant contextual observations and implications. The higher usability scores of ChatGPT and Gemini compared to Taguette can be attributed to their natural language interfaces, which simplify interaction by allowing users to engage conversationally. Unlike traditional tools like Taguette, which rely on manual tagging and coding workflows, chatbots eliminate the need for technical input by offering predictive and responsive assistance.