A/B Testing Without Real Users

Traditional A/B testing requires live traffic, which takes time and can be costly. With AI UX testing, we can simulate user interaction with two or more layouts before deployment — offering early UX feedback without needing real users.

Table of Contents

Step 1: Define Two Layout Variants

You can represent different layouts by modifying the grid environment or user goals.

# layout_a = goal in bottom-right corner
layout_a_goal = (9, 9)

# layout_b = goal moved to top-left
layout_b_goal = (0, 0)

Step 2: Create Separate Simulation Runs

Update your model to take a dynamic goal input:

def __init__(self, num_agents, width, height, goal_position):
    ...
    self.goal_position = goal_position

Step 3: Run Both Variants

# A Variant
model_a = UXModel(10, 10, 10, layout_a_goal)
for _ in range(20):
    model_a.step()

# B Variant
model_b = UXModel(10, 10, 10, layout_b_goal)
for _ in range(20):
    model_b.step()

Step 4: Compare the Results

Track and compare:

Number of agents who reached the goal
Average steps to complete the task
Most visited zones (visual heatmaps)

# Example metric
print("A Success:", sum([a.reached_goal for a in model_a.schedule.agents]))
print("B Success:", sum([a.reached_goal for a in model_b.schedule.agents]))

When to Use A/B Testing with AI

Redesigning landing pages
Testing different form flows
Choosing the best button placement

Benefits of AI UX Testing for A/B

This approach is faster, cost-effective, and allows you to test dozens of layout changes before running a real experiment. AI UX testing gives product teams early validation for their ideas.

Coming Up: Integrating AI UX Testing into Your Workflow

In Day 9, we’ll learn how to integrate this testing process into your product lifecycle, CI/CD pipelines, and design reviews.

Tag: #AIUXTesting #ABTesting #UXDesign

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