Discrimination/Slope Item Response Theory Models In item response theory (IRT), 1-PL models, which include a slope parameter in addition to the intercept parameter, are better able to discriminate test-taker talent. Higher slopes translate to steeper sigmoid curves.
But what does a highly discriminative test mean? Why would a steeper slope yield increased learnings about test-takers?

Intuitions are useful, but it is worth understanding the math behind the model. We model the probability of answering correctly to $i$-th question by a person with the ability $\theta$, $p_i({\theta})$. The model can have up to three parameters $a_i, b_i, c_i$ and uses a logistic function.
$$
p_i({\theta})=c_i + \frac{1-c_i}{1+e^{-a_i({\theta}-b_i)}}
$$
The first thing to notice when trying to understand it is that if you don't use the parameter $c_i$ (3PL), we can ignore the logistic function. The logistic function would map real values to the probability range $[0, 1]$ and change the shape from linear to sigmoidal, but it won't change the ordering of the values, so high after transformation was also high before, low after was low before, etc. After the transformation, the values are easier to interpret because they can be thought of as probabilities.
What remains is
$$
a_i(\theta-b_i)
$$
When ability $\theta$ is high, the question needs to be hard $b_i$ to make the value low. If the question is very hard $b_i$, it can make the outcome very low even if the ability $\theta$ was high. This is moderated by the $a_i$ slope, if it is small, it would make smaller everything inside the brackets, if it is big, it will make it bigger. Try it with different values and observe how it behaves for better intuition.
