AI
Apply AI: Using generative AI for creating test data samples
One powerful yet often overlooked application of generative AI is creating test data samples. Instead of manually crafting data sets, you can leverage tools like ChatGPT to generate realistic sample data quickly and efficiently.
This approach is especially useful when you’re learning frameworks like Python’s pandas and need a structured dataset to experiment with. By using a well-crafted prompt (or even not well crafted), you can produce diverse data samples tailored to your learning objectives.
For example, here’s a prompt I used with ChatGPT 4o to generate sample data:
I want to generate code for a test python DataFrame. The table has two columns Flavor and Scoops. The flavor column is persons favorite flavor. Number of scoops should stay between 1 and 3 scoops. Create 5 common flavors to be used for the test sample. Create 30 examples in the dataframe.
And this was the result.
import pandas as pd
import random
# Define common flavors and scoop range
flavors = ["Vanilla", "Chocolate", "Strawberry", "Mint Chocolate Chip", "Cookie Dough"]
# Generate sample data
data = {
"Flavor": [random.choice(flavors) for _ in range(30)],
"Scoops": [random.randint(1, 3) for _ in range(30)]
}
# Create DataFrame
df = pd.DataFrame(data)
Great example of using ChatGPT Deep Research for understanding zoning laws by state. Deep Research: Supermajority laws around the states
Apply AI: Creating a Quiz Generator in ChatGPT
ChatGPT can be utilized to create an adaptive multiple-choice quiz for students, progressively adjusting difficulty based on their answers while providing feedback and customization options.
Apply AI: Screening Social Media for Negative Behaviors in Application Processes
A friend’s son is applying to a private high school, and as part of the application process, the school requests links to any social media accounts he owns. I’ve heard of similar requirements in sorority recruitment and other selective social organizations.
With AI, it wouldn’t be difficult to analyze social media for potentially negative traits using image recognition models. These models could be trained to detect behaviors such as smoking, drinking, or making offensive hand gestures. For example, a student holding a red Solo cup in a photo could be flagged as a potential concern.
Beyond identifying negative behaviors, AI could also assess social tendencies. It could analyze how many photos feature the applicant alone versus in a group, or how many depict them playing sports, which might indicate teamwork and social engagement. This type of analysis could influence how schools or organizations evaluate an applicant’s personality and fit.