Artificial Intelligence and Learning Analytics in Teacher Education: A Systematic Review

Abstract

In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.