## 4 Results
### Trust Evaluation  
As presented in the methodology Section 3, trust was evaluated with the TOAST questionnaire. Altogether, 249 valid responses were obtained (three participants did not finish the trust survey--two responses were missing for Taguette evaluation and one for ChatGPT). Results were analyzed according to the two recognized subscales; performance and understanding. The reliability of the obtained results was measured with [PERSON]'s alpha and reached \(\alpha\) = 0.779 for items in the understanding subscale (items one, three, four, and eight of the questionnaire) and \(\alpha\) = 0.805 for items in the performance subscale (items two, five, six, seven, and nine of the questionnaire). Reliability remained high after observing the results per tools--for performance subscale \(\alpha_{\mathit{ChatGPT}}\) = 0.805, \(\alpha_{\mathit{Gemini}}\) = 0.853, \(\alpha_{\mathit{Taguette}}\) = 0.805, and understanding subscale \(\alpha_{\mathit{ChatGPT}}\) = 0.735, \(\alpha_{\mathit{Gemini}}\) = 0.826, \(\alpha_{\mathit{Taguette}}\) = 0.813. All the observed reliability values range from good to acceptable.  
The results of TOAST understanding and performance subscales per tools are presented in Figure 8. Observing the performance subscale, which indicates users' trust that the system will help them perform their task; Taguette reached the highest mean value with 5.38 (Std = 1.00), followed by ChatGPT with 5.17 (Std = 0.96), and Gemini with 5.10 (Std = 1.05). Therefore, users trusted Taguette the most to help them perform their UX evaluation tasks. Observing the understanding subscale, which indicates users' confidence about the calibration of their trust, ChatGPT reached the highest mean value with 5.88 (Std = 0.80), followed by Gemini with 5.53 (Std = 0.89), and Taguette with 5.47 (Std = 1.03). The results of understanding subscale are correlated with the participants' familiarity and frequency of use for AI tools, which was highest for ChatGPT and followed by Gemini inboth cases (data previously presented in Table 2). The users had previous experience with these two tools, which allowed them to calibrate their trust.  
The difference in both trust subscales between the tools was tested with a Kruskal-Wallis H test, which showed that there was a statistically significant difference in understanding subscale scores between the observed tool, \(\chi^{2}(2)=8.452\), \(p=0.015\), with a mean rank 143.58 for ChatGPT, 116.60 for Taguette, and 114.85 for Gemini. Additionally, Dunn's Test for pairwise comparison was performed, results of which are presented in Table 7 and showed a statistically significant difference in understanding subscale between the ChatGPT and Gemini (\(p=0.009\)) and Taguette and ChatGPT (\(p=0.015\)), meaning users were more sure in the calibration of their trust when using ChatGPT compared to Gemini and to Taguette, separately. No significant difference was observed between Taguette and Gemini. Kruskall-Wallis H test additionally showed a statistically significant difference on the performance subscale with \(\chi^{2}(2)=4.515,p=0.105\), and mean rank 138.48 for Taguette, 120.95 for ChatGPT, and 115.85 for Gemini. Dunn's Test showed a statistically significant difference on a performance subscale between Gemini and Taguette (\(p=0.042\)), meaning users trusted Taguette more to help perform their tasks, compared to Gemini. The effect size of both Kruskal-Wallis tests was quite small with \(\eta_{\mathit{understanding}}^{2}=0.026\) and \(\eta_{\mathit{performance}}^{2}=0.010\).