Agentic AI for Education: A Unified Multi-Agent Framework for Personalized Learning and Institutional Intelligence
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908975248703488 |
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| author | J, Arya Mary K Bhaskar, Deepthy K S, Sinu T P, Binu V |
| author_facet | J, Arya Mary K Bhaskar, Deepthy K S, Sinu T P, Binu V |
| contents | Agentic Artificial Intelligence (AI) represents a paradigm shift from reactive systems to proactive, autonomous decision making frameworks. Existing AI-based educational systems remain fragmented and lack multi-level integration across stakeholders. This paper proposes the Agentic Unified Student Support System (AUSS), a novel multi-agent architecture integrating student-level personalization, educator-level automation, and institutional-level intelligence. The framework leverages Large Language Models (LLMs), reinforcement learning, predictive analytics, and rule-based reasoning. Experimental results demonstrate improvements in recommendation accuracy (92.4%), grading efficiency (94.1%), and dropout prediction (F1-score: 89.5%). The proposed system enables scalable, adaptive, and intelligent educational ecosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16566 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic AI for Education: A Unified Multi-Agent Framework for Personalized Learning and Institutional Intelligence J, Arya Mary K Bhaskar, Deepthy K S, Sinu T P, Binu V Multiagent Systems Agentic Artificial Intelligence (AI) represents a paradigm shift from reactive systems to proactive, autonomous decision making frameworks. Existing AI-based educational systems remain fragmented and lack multi-level integration across stakeholders. This paper proposes the Agentic Unified Student Support System (AUSS), a novel multi-agent architecture integrating student-level personalization, educator-level automation, and institutional-level intelligence. The framework leverages Large Language Models (LLMs), reinforcement learning, predictive analytics, and rule-based reasoning. Experimental results demonstrate improvements in recommendation accuracy (92.4%), grading efficiency (94.1%), and dropout prediction (F1-score: 89.5%). The proposed system enables scalable, adaptive, and intelligent educational ecosystems. |
| title | Agentic AI for Education: A Unified Multi-Agent Framework for Personalized Learning and Institutional Intelligence |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2604.16566 |