Artificial intelligence for human learning: A review of machine learning techniques used in education research and a suggestion of a learning design model

Abstract

The goal of this research is to (1) identify the status and development of AI and ML-based learning support systems and their impact on human learning, with a specific focus on techniques employed in previous research, and (2) demonstrate the process of designing a learning support system using AI. Artificial intelligence (AI) and machine learning (ML) technologies have received attention in education. The existing research on AI in education is examined, considering the implications of its application in research. Noteworthy ML techniques from the literature are explained, followed by a discussion on leveraging AI and ML technologies to enhance learning support. Additionally, with consideration of both front-end and back-end approaches,a framework for incorporating AI into education is proposed. Subsequently, a learning design model, Self-regulated Learning with AI Assistants (SLAA), is suggested for addressing the objectives of AI-based learning support system design. The categorization of AI and ML techniques in education research reveals nine types, including supervised learning, mining approaches, and Bayesian techniques. The exploration illustrates how these techniques can be employed in designing a learning support system. This paper provides an empirical overview of AI in education, addresses technological and pedagogical considerations for developing personalized and adaptive learning environments, and outlines the challenges and potential future research directions.