Choose Wisely: Leveraging Explainable AI to Support Reflective Decision-Making

Explainable Artificial Intelligence (XAI) can contribute to the idea of AI being an instrument for reflection when used for augmentation of human decision-making. In the educational domain, reflective decision-making is crucial as decisions have a meaningful and long-term impact. Against this background, we propose an XAI-based approach that supports users in making reflective educational decisions. Our approach introduces three main ideas: concepts as a “shared language” between AI and users, concept-based explanations, and concept-based interventions. We demonstrate the practical applicability of our approach for a real-world dataset with university courses. We evaluate the efficacy of our approach in a user study with 495 participants. Results suggest that our novel approach effectively supports users in making reflective decisions compared to black box recommender systems, while increasing users' exploration, self-reflection, confidence, and trust. The effectiveness of our approach is attributable to the combination of concept-based explanations and the opportunity to intervene.