%0 Journal Article %1 KHOSRAVI2022100074 %A Khosravi, Hassan %A Shum, Simon Buckingham %A Chen, Guanliang %A Conati, Cristina %A Gasevic, Dragan %A Kay, Judy %A Knight, Simon %A Martinez-Maldonado, Roberto %A Sadiq, Shazia %A Tsai, Yi-Shan %D 2022 %J Computers and Education: Artificial Intelligence %K artificialintelligence education ethics explainableAI learninganalytics openlearnermodels trust %P 100074 %R https://doi.org/10.1016/j.caeai.2022.100074 %T Explainable Artificial Intelligence in education %U https://www.sciencedirect.com/science/article/pii/S2666920X22000297 %X 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 developing educational AI tools. These key aspects focus on the stakeholders, benefits, approaches for presenting explanations, widely used classes of AI models, human-centred designs of the AI interfaces and potential pitfalls of providing explanations within education. We then present four comprehensive case studies that illustrate the application of XAI-ED in four different educational AI tools. The paper concludes by discussing opportunities, challenges and future research needs for the effective incorporation of XAI in education.

@article{KHOSRAVI2022100074, abstract = {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 developing educational AI tools. These key aspects focus on the stakeholders, benefits, approaches for presenting explanations, widely used classes of AI models, human-centred designs of the AI interfaces and potential pitfalls of providing explanations within education. We then present four comprehensive case studies that illustrate the application of XAI-ED in four different educational AI tools. The paper concludes by discussing opportunities, challenges and future research needs for the effective incorporation of XAI in education.}, added-at = {2022-05-22T11:22:22.000+0200}, author = {Khosravi, Hassan and Shum, Simon Buckingham and Chen, Guanliang and Conati, Cristina and Gasevic, Dragan and Kay, Judy and Knight, Simon and Martinez-Maldonado, Roberto and Sadiq, Shazia and Tsai, Yi-Shan}, biburl = {https://www.bibsonomy.org/bibtex/259133e526641afe4de0648260ba01dcc/ereidt}, doi = {https://doi.org/10.1016/j.caeai.2022.100074}, interhash = {6605ea99ce675765f9d16c5a13e7db69}, intrahash = {59133e526641afe4de0648260ba01dcc}, issn = {2666-920X}, journal = {Computers and Education: Artificial Intelligence}, keywords = {artificialintelligence education ethics explainableAI learninganalytics openlearnermodels trust}, pages = 100074, timestamp = {2022-05-22T11:22:22.000+0200}, title = {Explainable Artificial Intelligence in education}, url = {https://www.sciencedirect.com/science/article/pii/S2666920X22000297}, year = 2022 }