Theoretical models of AI in student-centered education: a systematic literature review

Abstract

The use of artificial intelligence (AI) in education has garnered heightened interest, particularly in facilitating student-centered learning. This research conducts a thorough evaluation of theoretical models and empirical investigations about the implementation of AI in secondary education from 2020 to 2025. The 15 high-quality publications were selected from the Scopus and Web of Science (WoS) databases according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) procedure and analyzed thematically. The findings indicated that the present incorporation of AI in education predominantly depends on 11 fundamental theoretical frameworks, such as self-determination theory (SDT), theory of planned behavior (TPB), technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), and sociocultural theory (SCT). For example, SDT emphasizes students’ motivation and psychological needs, the TPB explains behavioral intentions for using AI, and TAM/UTAUT is used to explain students’ willingness and behavior in using AI tools. However, the application of AI still faces numerous challenges, including anxiety and ethical dilemmas. This study clarifies the correspondence between theory and practice, providing a theoretical foundation for educators to conduct instructional design and support work, and offering a reference for policymakers to develop AI education standards and allocate resources.