Key points are not available for this paper at this time.
Explainable Artificial Intelligence (XAI) plays a pivotal role in enhancing adaptive learning systems by fostering an environment where transparency is key to developing students' critical thinking skills. In such systems, each learner's experience is uniquely tailored, not just to their current knowledge and cognitive abilities, but also with the intent of expanding their reasoning and problem-solving capacities. The crux of XAI within these educational contexts lies in its ability to demystify AI decisions. This transparency allows learners to grasp the "why" and "how" of the AI-driven guidance they receive, thereby promoting a deeper level of self-reflection and metacognitive awareness. The integration of XAI into platforms like RiPPLE exemplifies this approach, where the system leverages crowdsourced data and learning science to offer personalized activity recommendations. By aligning these recommendations with individual knowledge states, and elucidating the underlying logic, XAI-equipped adaptive learning environments not only cater to the immediate educational needs but also lay the groundwork for cultivating autonomous and discerning thinkers.
Ermira Idrizi (Sat,) studied this question.