The utilization of reinforcement learning (RL) within the field of education holds the potential
to bring about a significant shift in the way students approach and engage with learning and …

This survey article has grown out of the RL4ED workshop organized by the authors at the
Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a …

We explore eXplainable AI (XAI) to enhance user experience and understand the value of
explanations in AI-driven pedagogical decisions within an Intelligent Pedagogical Agent …

Reinforcement learning (RL) is broadly employed in human-involved systems to enhance
human outcomes. Off-policy evaluation (OPE) has been pivotal for RL in those realms since …

Teachers' ability to self-regulate their own learning is closely related to their competency to
enhance self-regulated learning (SRL) in their students. Accordingly, there is emerging …

Reinforcement learning (RL) has been extensively researched for enhancing human-
environment interactions in various human-centric tasks, including e-learning and …

A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to
induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) …

In the realm of reinforcement learning (RL), off-policy evaluation (OPE) holds a pivotal
position, especially in high-stake human-involved scenarios such as e-learning and …

Disease progression modeling (DPM) plays an essential role in characterizing patients'
historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to …

Apprenticeship learning (AL) is a process of inducing effective decision-making policies via
observing and imitating experts' demonstrations. Most existing AL approaches, however, are …