Recommender systems for technology-enhanced learning are examined in relation to
learners' agency, that is, their ability to define and pursue learning goals. These systems …

Explainable AI (XAI) has been investigated for decades and, together with AI itself, has
witnessed unprecedented growth in recent years. Among various approaches to XAI …

A significant challenge for recommender systems (RSs), and in fact for AI systems in
general, is the systematic definition of explanations for outputs in such a way that both the …

As AI systems become ever more intertwined in our personal lives, the way in which they
explain themselves to and interact with humans is an increasingly critical research area. The …

The education sector has drastically changed from offline to online mode in the past few
years. Educational data mining, learning analytics, and machine learning are the fields that …

Argumentative explainable AI has been advocated by several in recent years, with an
increasing interest on explaining the reasoning outcomes of Argumentation Frameworks …

Recent advancements in web personalization techniques facilitate enhanced web-based
services that allow recommender systems (RSs) to incorporate contextual knowledge about …

As a practical mechanism for formalizing commonsense reasoning, argumentation has
shown its potential for applications in diverse areas, many related to decision-making in …

Recent works in Explainable AI mostly address the transparency issue of black-box models
or create explanations for any kind of models (ie, they are model-agnostic), while leaving …

Nowadays, virtual learning environments have become widespread to avoid time and space
constraints and share high-quality learning resources. As a result of human–computer …