## 5 Implementation and User Scenario
### Diverse User Scenarios: Saudi Arabian E-Commerce  
The AI-MDD-UX is demonstrated through its dynamic UI adaptation in the context of Saudi Arabian E-Commerce. The first interaction for users with an e-commerce platform is the Main GUI Interface. In this scenario, [PERSON], a business manager, seeks to personalize the shopping experience for users of his e-commerce platform, Smartshop. The UI should adapt based on user profiles, such as fast shopper, detail-oriented shopper, or discount seeker. Instead of designing multiple UI variations manually, [PERSON] integrates AI-MDD-UX to automate the UI generation, adaptation, and optimization process.  
To illustrate the principle of AI-MDD-UX, let's follow [PERSON], who is a detail-oriented shopper with a history of purchasing home electronics. Currently, he is browsing Smartshop, having previously bought a smart speaker and shown interest in smart home devices. Below are key scenarios that demonstrate the AI-driven UI adaptation process:  
**Scenario 1# Initial Interaction and UI Adjustment**  
When [PERSON] logs into Smartshop, the GAN model generates a personalized main UI layout, displaying recommended electronic products. Based on his browsing history, the system prioritizes smart home devices such as smart lights and thermostats (Figure 7). As [PERSON] scrolls through the product list, the MAS system continuously tracks his interactions in real time by monitoring time spent on specific products, clicks, checkout behaviors and hover interactions over CTA buttons like \"Add to Cart\" and \"Similar Products\".  
**Scenario 2# Adaptative CTA**  
The system detects that [PERSON] hovers over a smart thermostat but does not click on it. Recognizing interest without immediate action, the early guidance strategy is activated. The MAS layer applies the adaptive CTA rule, enlarging the smart thermostat's CTA button and changing its color to a more attention-grabbing shade of blue. These UI adjustments are sent to the RL layer, which learns that early engagement with UI elements increases click-through rates. The rendering layer immediately updates [PERSON]'s UI with the newly adjusted CTA buttons (see Figure 8).  
Figure 7: Main GUI of the e-commerce platform.  
**Scenario 3# Predictive Product Placement**  
After adding the smart thermostat to his cart, the MAS layer applies the predictive guidance and triggers a predictive product placement rule. The RL layer anticipates that users who add smart thermostats to their cart are also likely to purchase home automation hubs. By analyzing [PERSON]'s purchase history, the RL layer refines its prediction. The rendering layer updates the UI to prominently display a recommended smart home hub, increasing the likelihood of an additional purchase (Figure 9).  
**Scenario 4# Purchase Completion and Continuous Learning**  
Figure 8: Adjustment of CTA buttons.  
Figure 9: Predicted products with similar profiles.  
[PERSON] completes his purchase. The MAS layer analyzes his engagements, detecting positive feedback. The RL layer updates its reward function to reinforce this positive outcome Additionally, the MAS layer evaluates [PERSON]'s overall experience, particularly his engagement with UI adjustments and product recommendations. The Continuous Improvement Layer detects that [PERSON] responded well to the early visualization cue of enlarged CTA buttons. Based on this learning, the RL system refines its UI Adjustment Rule, ensuring more effective adaptations for future users with similar interaction patterns (Figure 10).