Abstract:

One of the central challenges in education is providing personalised learning experience to every pupil. A widely reported approach has been to automate teaching using digital learning systems that take advantage of recent progress in artificial intelligence (AI). Providing personalised learning experience using AI has shown promising effects on grades and student engagement levels. These systems take into account the level of student knowledge, recommend learning content and optimise the learning path towards future goals. Although intelligent learning systems seem to be an elegant way of bringing quality education to the masses, it is necessary to be aware of the assumptions these systems are based on. While learning systems play an important role in learner-centered education, they have fundamental limitations and caveats that are unattended in current literature. We provide an overview of assumptions in the available literature of personalised education technologies and artificial intelligence in education (AIEd). We carefully examine the implications of these assumptions to reveal the fundamental problems in the current direction. We hope that this timely and relevant analysis of the current direction of educational technology enhances the future perspective of intelligent learning systems.

Keywords:

Personalised education, educational technology, artificial intelligence in education.