Explainable Artificial Intelligence in education

There are emerging concerns about the Fairness, Accountability, Transparency, and Ethics (FATE) of educational interventions supported by the use of Artificial Intelligence (AI) algorithms. We then present four comprehensive case studies that illustrate the application of XAI-ED in four different educational AI tools.

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• 1 One of the emerging methods for increasing trust in AI systems is to use eXplainable AI (XAI), which promotes the use of methods that produce transparent explanations and reasons for decisions AI systems make.
• 2 Considering the existing literature on XAI, this paper argues that XAI in education has commonalities with the broader use of AI but also has distinctive needs.
• 3 Accordingly, we first present a framework, referred to as XAI-ED, that considers six key aspects in relation to explainability for studying, designing and developing educational AI tools.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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