EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning

As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use explainable to inform stakeholders about reasons behind such has been gaining much attention a wide range fields, including education. Also, field education there long history research into self-explanation, where students explain process their answers. This recognized as beneficial intervention promote metacognitive skills, however, is also unexplored potential gain insight problems that learners experience due inadequate prerequisite knowledge skills are required, or application task at hand. While this aspect self-explanation interest teachers, little information educational AI systems. In paper, we propose system which both each other were made, as: student cognition during answering process, explanation based on internal mechanizes abstract representations model algorithms.