Harnessing Artificial Intelligence for Innovation in Education

Abstract

AbstractIn the field of educational technology, Artificial Intelligence in Education (AIEd) is an emerging field that is projected to have a profound impact on the teaching and learning process. The AIEd has already been around for more than 30 years, but educators may still have concerns about scaling the pedagogical benefits of the AIEd and how it could positively impact the teaching and learning process. The purpose of this chapter is to demystify artificial intelligence (AI), its impact on society and how to harness the power of AI for transformational change in education. Taking the first step is clarifying the definition of artificial intelligence (AI) to differentiate it from human intelligence (HI). With this understanding in place, an open learner model by design can be applied as a framework which explains how AI can be used to enhance teaching and learning in general (Luckin et al., 2016). It is the purpose of this chapter to advocate for teachers’ roles to be augmented and evolved to be AIEd-enabled, and to consider AIEd applications from three different perspectives: (i) learner-facing, (ii) teacher-facing and (iii) system-facing AIEd (Baker and Smith, 2019). There has been significant progress in the area of student-facing AIEd, especially when it comes to the development of personalized adaptive learning systems based on big learning data. The open model adaptive system as presented by Luckin et al. (2016) provided insights into the design of a learner-facing, personalized learning system. It was discussed that a personalized adaptive learning system (PALS) framework was proposed as an example of how artificial intelligence can be applied to a situation for student-facing purposes (Palanisamy et al., 2021). There are two aspects of teacher-facing AIEd that have garnered a lot of interest: automatic grading and prompt feedback on the learners’ progress. As a system-facing solution, AIEd offers academic administrators insights into learners’ profiles and predictions, admission decisions and course scheduling, attrition and retention and student models and academic achievement. An evaluation of the literature on AIEd suggests that the future of AIEd is intertwined with the ability of AI to be integrated with other emerging technologies, like immersive technology and the Internet of Things, to create new innovations in teaching and learning.