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. 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. 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. Accordingly, we first present a framework, referred to as XAI-ED, that considers six key aspects in relation to explainability for studying, designing and…

• JF
  Jacobs Foundation
• NS
  Natural Sciences and Engineering Research Council of Canada
• EA
  Economic and Social Research Council

  Award: ES/S015701/1
• AR
  Australian Research Council

  Awards: DP220101209, DP210100060, IC200100022