Teacher intervention in K-12 AI-based instruction: a systematic review of processes, strategies, and effects

Abstract

The rapid advancement of artificial intelligence has made AI-based instruction, including intelligent tutoring systems, learning analytics dashboards, adaptive learning platforms, and generative AI, increasingly prevalent in K-12 education. However, AI-generated information does not automatically lead to pedagogical action; it gains instructional meaning only when teachers interpret, judge, and translate it into situated classroom support. Following PRISMA guidelines, this systematic review analyzed 29 peer-reviewed English-language studies published between 2016 and 2025 across six databases to examine how teacher intervention is constituted in K-12 AI-based instruction and what conditions shape its reported effects. The synthesis was organized around three dimensions: process, strategy, and effect. Findings indicate that teacher intervention operates as a cyclical process of monitoring, judgment, intervention, and orchestration, through which AI-generated information is transformed into pedagogical action and subsequent classroom adjustment. Three broad intervention strategies were identified: pedagogical translation of AI outputs, design of learning support, and reconstruction of interaction structures. Reported benefits for student learning and teacher orchestration were generally promising but conditional, varying according to the interpretability of AI information, intervention timing, target level, teachers’ implementation feasibility, and students’ autonomy. By presenting an integrated conditional framework, this review argues that the teacher’s role in AI-based instruction is being reconfigured rather than diminished, offering implications for teacher professional development and the design of teacher-facing AI systems.