Generative AI-powered agentic systems are increasingly proposed for educational applications. However, the research landscape remains fragmented, and the relationship between technical capabilities and pedagogical design remains poorly understood. To address this gap, this scoping review systematically mapped 474 studies published between January 2020 and May 2026. Guided by three research questions, we analysed publication characteristics, study designs, agent roles, AI models and architectures, six dimensions of agentic capability, and the extent to which educational theory was incorporated. The results show that the field has expanded rapidly, particularly since 2025. At the same time, the literature is dominated by conference papers and is primarily concentrated in higher education, STEM disciplines, and text-based tutoring scenarios. In terms of technical implementation, GPT-series models and LangChain are the most widely adopted technologies, whereas OpenClaw and other frontier agent paradigms remain largely absent. Across the reviewed studies, agentic capabilities tend to remain at relatively modest levels: although many systems demonstrate single-task autonomy, sequential planning, and, increasingly, multi-agent collaboration, they rarely exhibit strong tool orchestration or robust embedded governance. From an educational perspective, theoretical grounding remains limited. Only 138 studies explicitly drew on educational theory, revealing a clear disciplinary divide between technically oriented research and pedagogically oriented work. Methodologically, empirical evaluations are also limited, with most studies relying on small-scale and short-term designs. Accordingly, the gaps identified across the literature converge on several priorities: longitudinal and real-world validation, stronger pedagogical grounding, more governed adoption of emerging agent infrastructures, and more systematic integration of ethics and human oversight. Overall, this review provides researchers, developers, and educators with an evidence-based map of the current capabilities and limitations of agentic AI in education, while also highlighting concrete directions for its more responsible and educationally meaningful development.
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