Counterfactual Explanations in Education: A Systematic Review

Abstract

Counterfactuals are a type of explanations based on hypothetical scenarios used in Explainable Artificial Intelligence (XAI), showing what changes in input variables could have led to different outcomes in predictive problems. In the field of education, counterfactuals enable educators to explore various hypothetical scenarios, facilitating informed decision-making and the application of educational strategies for improving students' academic performance or reducing dropout rates, among others. Despite the gradual expansion of research on counterfactuals in education, systematic literature reviews on this topic remain scarce. The identification of the most relevant advancements in this field can provide a deep insight into the current state of research, highlighting the most effective areas and revealing opportunities for future studies. The objective of this research is to conduct a systematic literature review, using the PRISMA methodology, to analyze three aspects regarding the use of counterfactuals in education: the problems that counterfactuals help to address in education, the methods and/or algorithms used to generate them, and how the counterfactuals are presented in the educational context. As a result, we have identified a series of key challenges and opportunities for future research over the next few years, which constitute the main contribution of this paper. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI.