Systematic Literature Review: Explainable AI Definitions and Challenges in Education
It addresses the lack of standardized XAI definitions and challenges for educators and researchers, but is incremental as a literature review.
This systematic review examined definitions and challenges of Explainable AI (XAI) in education, identifying 15 definitions and 62 challenges categorized into seven groups, such as explainability and ethics, to enhance understanding in the field.
Explainable AI (XAI) seeks to transform black-box algorithmic processes into transparent ones, enhancing trust in AI applications across various sectors such as education. This review aims to examine the various definitions of XAI within the literature and explore the challenges of XAI in education. Our goal is to shed light on how XAI can contribute to enhancing the educational field. This systematic review, utilising the PRISMA method for rigorous and transparent research, identified 19 relevant studies. Our findings reveal 15 definitions and 62 challenges. These challenges are categorised using thematic analysis into seven groups: explainability, ethical, technical, human-computer interaction (HCI), trustworthiness, policy and guideline, and others, thereby deepening our understanding of the implications of XAI in education. Our analysis highlights the absence of standardised definitions for XAI, leading to confusion, especially because definitions concerning ethics, trustworthiness, technicalities, and explainability tend to overlap and vary.