The intuition behind this network is that the $f_t$ can be used to infer the student ability on $q_t$ by further processing $f_t$ using network and the difficulty level of $q_t$ can be measured by passing $k_t$ to network. This two networks can be expressed as: $\theta_{tj} = tanh (W_{\theta}f_t + b_{\theta})$- student ability

$\beta_j = tanh (W_{\beta}q_t + b_{\beta})$ - difficulty level

Then these two values are passed to IRT model to calculate the probability that student will answer knowledge component correctly:

$p_t = \sigma (3.0 * \theta_{tj} - \beta_j)$

Both of these networks can applied to any types of neural networks.