Why Visualization Matters in AI UX Testing

Raw numbers and logs can be difficult to interpret. By visualizing AI UX testing behavior, you can clearly see where users go, where they get stuck, and what patterns emerge — making it easier to convince stakeholders and make UX decisions.

Table of Contents

Tools We’ll Use

Matplotlib: Great for bar charts and line plots
Seaborn: Adds nicer defaults and statistical visualizations
NumPy: Useful for matrix-style heatmaps

Make sure you’ve installed them:

pip install matplotlib seaborn numpy

1. Heatmap of User Activity

Show which parts of the screen/layout are most interacted with.

import matplotlib.pyplot as plt
import numpy as np

width, height = 10, 10
heat = np.zeros((height, width))
for (x, y), count in model.cell_visits.items():
    heat[y][x] = count

plt.imshow(heat, cmap="hot", interpolation="nearest")
plt.title("User Activity Heatmap")
plt.colorbar()
plt.show()

2. Bar Chart: Top Cells Visited

import seaborn as sns

top_cells = sorted(model.cell_visits.items(), key=lambda x: x[1], reverse=True)[:10]
labels = [f"{pos}" for pos, _ in top_cells]
counts = [count for _, count in top_cells]

sns.barplot(x=counts, y=labels)
plt.title("Top 10 Visited Cells")
plt.xlabel("Visits")
plt.ylabel("Grid Position")
plt.show()

3. Line Plot: Steps Over Time

Show how many agents reached the goal per step:

steps = list(range(len(goal_counts)))
plt.plot(steps, goal_counts)
plt.title("Agents Reaching Goal Over Time")
plt.xlabel("Simulation Step")
plt.ylabel("Cumulative Completions")
plt.show()

Visuals Make AI UX Testing Actionable

With clear visuals, your AI UX testing results become easier to communicate to product teams and clients. You’ll know which areas are working — and which need redesign.

Next Up: Testing A/B UX Variants with AI

Tomorrow in Day 8, you’ll simulate and compare different layout options using the same AI UX testing model — no real users needed!