Explainable AI for Intelligent Tutoring Systems

Abstract

This paper investigates the significance of Explainable AI(XAI) in educational technologies, employing the iRead project as a case study. The iRead project, an adaptive learning technology designed to bolster language learning, demonstrates the pivotal role of explainability in fostering effective, trustworthy, and engaging learning environments. Its transparent design, which adapts to individual learners, exemplifies the additional importance of explainability in facilitating personalized instruction and in managing classroom diversity. Observations across multiple pilot programs reveal that the iRead project significantly improves learners’ motivation, engagement, and literacy skills. Moreover, the clarity of its adaptive algorithms has eased teachers’ concerns, promoting its effective integration into classroom instruction. Beyond enhancing literacy skills, the project has also improved digital literacy among both teachers and students. This case study substantiates the need for transparent and understandable AI tools in fostering effective and inclusive educational experiences, highlighting their value in modern pedagogical approaches.

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