## 4 Discussion
### Lessons Learned from the Design and Evaluation of Each Application Case Study
However, in this case, it was applied on the prototype by representative end-users, showing that this method has a more versatile use than it was originally designed for. In addition to qualitative feedback (e.g., participants mentioning that windy weather made the app harder to use, or that arthritis affected tapping accuracy), the team also quantitatively collected data measures to summarize how effectively the walkthrough met user needs. For instance, the finding that 80% of participants had never tried AR before yet still found the instructions easy, is a helpful indicator when interpreting the overall usability score. The project used the statistical within-subjects design--meaning everyone experienced the same tasks--so that any variations in performance or preference scores minimized the effect of individual differences and increased the statistical power of the test. The participants' CW data, combined with the direct observation data, provided a solid overview of what worked well and what required refinement to make the AR experience even more accessible. Combining quantitative and qualitative methods allows researchers to gain a fuller understanding of user experiences and product performance. Quantitative data--suchas completion times, error counts, or SUS questionnaire scores--offers measurable, objective metrics that can be compared, tracked over time, or statistically analyzed. This helps determine whether design changes lead to clear improvements and provides evidence for stakeholders who require numerical results. On the other hand, qualitative feedback--through interviews, open-ended survey responses, or observations--highlights the \"why\" behind the data. It captures nuanced opinions, emotional responses, and unforeseen usability hurdles that purely numerical measures might overlook. By merging both approaches, teams can validate their hypotheses with hard evidence while also uncovering deeper insights into user motivations and behaviors, leading to more informed, user-centered design decisions. Although not all case studies in this article included quantitative data, future work should explore more consistent numerical evaluation across projects to enable broader comparisons. The Hanging Gardens of Babylon use case review showcased the usefulness of an A/B test design to compare two versions of a user experience, like in this case, comparing an AR app versus a VR app. This type of test design offers a clear and systematic approach for gauging which option better meets specific goals--in this case, learning retention. By dividing participants into two distinct groups (group A for one design choice, and group B for the alternative design choice), researchers can isolate how each interface impacts performance on the same tasks, ensuring more credible comparisons. Each group then completes identical tasks and takes a comprehension quiz, allowing direct measurement of the user's task performance under both conditions. This method is particularly valuable for complex choices between different UXUI solutions because it controls for variables like participant differences or fluctuating conditions by making both groups perform comparable actions. The resulting data highlight how effectively each interface supports the intended outcome, whether it is improved recall, faster task completion, or greater user satisfaction. In this case, the surprising finding that AR led to better retention than VR contradicts earlier research, emphasizing the importance of empirical, context-specific evaluations. Ultimately, an A/B test design provides straightforward, quantitative insight into which option excels, guiding teams to refine or adopt the strongest UXUI for their objectives. The Weigh House Estonia application use case evaluation setup is typically labeled a \"field-based, qualitative\" user study with a multi-phase design.