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📄 Research Article

Agentic AI in Education: A Scoping Review of Research Landscape, Capabilities, and the Frontier Agent Paradigm

● high · 2026-07-29agentic-ai, ai-literacy, educational-theory, generative-ai, higher-ed, intelligent-tutoring, llm, multi-agent, open-source, scaffolding

Agentic AI in Education: Scoping Review (474 Studies, 2020–2026)

Published in Computers and Education: Artificial Intelligence, accepted 27 July 2026.

📄 doi:10.1016/j.caeai.2026.100653

Summary

This scoping review systematically maps 474 studies (January 2020 – May 2026) on generative AI-powered agentic AI in education, providing the most comprehensive synthesis of the field to date. The authors analyze publication characteristics, study designs, agent roles, AI models/architectures, six dimensions of agentic capability, and the extent of educational theory integration.

Key Findings

1. Rapid Expansion Since 2025

The field has grown explosively, but the literature is dominated by conference papers concentrated in higher education, STEM disciplines, and text-based tutoring scenarios. This mirrors the general trajectory of AI Education research, but with a specific agentic inflection point in 2025.

2. Technology Stack: GPT + LangChain Dominate

GPT-series models and LangChain are the most widely adopted technologies. Notably, OpenClaw and other frontier agent paradigms (governed tool orchestration, persistent memory, long-horizon planning, multi-agent coordination) remain largely absent from educational research — revealing a significant technology–application gap. This stands in contrast to the vision articulated in Agentic AI.

3. Agentic Capabilities Remain Modest

Across the six capability dimensions analyzed:

  • Single-task autonomy — commonly demonstrated ✓
  • Sequential planning — increasingly present ✓
  • Multi-agent collaboration — growing ✓
  • Strong tool orchestration — rarely exhibited ✗
  • Robust embedded governance — rarely exhibited ✗
  • Persistent memory / long-horizon planning — largely absent ✗

    • This maps closely to the four-paradigm framework in Agentic Workflows Education (reflection, planning, tool use, multi-agent collaboration), where the reviewed systems tend to cluster in the first two paradigms while falling short on the more advanced ones.

    4. Theoretical Grounding is Limited

    Only 138 of 474 studies (29%) explicitly drew on educational theory, revealing a clear disciplinary divide between technically oriented research (CS/engineering) and pedagogically oriented work (education/learning sciences). This echoes broader concerns in Principled AI Education about the gap between technological capability and pedagogical intentionality.

    5. Methodological Limitations

    Most studies rely on small-scale, short-term designs. Longitudinal and real-world validation studies are rare, limiting the evidence base for claims about effectiveness. The review calls for more rigorous efficacy-study designs and attention to Student Experience beyond immediate performance metrics.

    6. Six Dimensions of Agentic Capability (the Review's Analytical Framework)

    DimensionDescriptionStatus in Reviewed Studies
    Task AutonomyIndependent task initiation, planning, completionCommon
    Goal-Directed ReasoningStrategy selection and adaptation to contextEmerging
    Memory & Context AwarenessUsing interaction history and learner profilesLimited
    Planning & SequencingMulti-step plan formulation and executionGrowing
    Tool OrchestrationInvoking and coordinating external tools/resourcesRare
    Governance & OversightAuditable action, safety constraints, human-in-the-loopRare

    The governance gap is particularly concerning given frameworks like Human In The Loop AI, which emphasize that educational AI systems require robust oversight mechanisms — not just technical capability.

    Priority Research Directions

    The review identifies several converging priorities:

    1. Longitudinal and real-world validation — moving beyond short-term lab studies

    2. Stronger pedagogical grounding — bridging the CS/education disciplinary divide

    3. Governed adoption of emerging agent infrastructures — particularly tool orchestration and multi-agent coordination

    4. Systematic integration of ethics and human oversight — connecting to Equity and Academic Integrity concerns

    5. Expanding beyond STEM and higher education — into K-12, language learning, special education, and professional training contexts

    OpenClaw as an Analytical Lens

    The review uses OpenClaw (Steinberger, 2026) — the fastest-growing Open Source AI project in early 2026 — as an illustrative reference point for the "frontier agent paradigm": systems that feature governed tool orchestration via MCP, persistent memory, long-horizon planning, multi-agent coordination, and auditable action. The finding that these capabilities are largely absent from educational agentic systems is the review's most striking technology–application gap. While the authors are careful not to position OpenClaw as a normative target, its feature set serves as a useful benchmark for assessing how far educational systems lag behind general-purpose agentic infrastructure.

    Connected Concepts

  • Adaptive Learning
  • Agentic AI
  • Human In The Loop AI
  • AI Education
  • Open Source
  • Student Experience
  • Agentic AI
  • AI Literacy
  • Generative AI
  • Higher Ed
  • LLM
  • Scaffolding

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    • Citation

    Wang, N., Zou, D., Xie, H., & Qin, S. J. (2026). A scoping review of generative AI-powered agentic AI in education: Research landscape, agentic capabilities, and insights from the frontier agent paradigm, exemplified by OpenClaw.