Date
Publisher
arXiv
Integration of artificial intelligent (AI) agents in higher education is transforming teaching, learning and administrative processes. Although existing AI agents effectively support individual tasks, their implementation remains fragmented and inefficient for handling the complexity of educational institutions. This highlights a significant research gap: the lack of integrated eco-system-level agentic multi-agent AI platform capable of coordinated planning, reasoning, and adaptive decision-making across multiple educational functions. This paper presents a forward-looking perspective on agentic multi-agent AI platform in higher education, consisting interconnected autonomous, goal driven agents that support learning, teaching, and institutional operations. It addresses timely and critical questions: Can agentic AI represent the next generation of intelligent systems in tertiary education? Can they collectively support seamless coordinated operations across teaching, learning and administrative support? To what extent can such systems foster inclusive and equitable learning for diverse learners with special educational needs? To ground this perspective, a thematic analysis of existing literature identifies four dominant themes: task-specific fragmented AI tools, the transition from single-agent to multi-agent systems, limited cross-functional integration, and insufficient focus on inclusivity and accessibility. Findings reveal a clear gap between current AI implementations and the needs of holistic, learner-centered educational ecosystem. The paper synthesizes challenges and outlines future research directions for scalable human-aligned, and inclusive agentic AI platform. The significant contribution is the incorporation of inclusive learning perspectives, highlighting how coordinated agentic multi-agent platform can support diverse learners through adaptive, multimodal interventions.
Study at a glance
- Question: Can coordinated, multi-agent AI ecosystems support inclusive teaching, learning, and administrative functions in higher education?
- Setting and data: 48 published studies examining AI agents and agentic systems in undergraduate and higher education.
- Method: Thematic analysis and evidence synthesis of published literature using an inductive coding approach.
Why it matters
Existing AI agents support individual educational tasks effectively, but remain fragmented and lack integrated coordination across complex institutional functions. Crucially, current systems often insufficiently address the needs of diverse learners with special educational needs.
Key findings
- Approximately 52% of reviewed studies focused on task-specific AI tools and 28% used single-agent systems, while multi-agent systems (12%) and agentic multi-agent systems (8%) were rarely explored.
- Across the reviewed literature, AI systems exhibited a limited and inconsistent focus on inclusive learning principles and special educational needs, often treating inclusion as implicit rather than explicitly designed.
- Reviewed implementations predominantly operated in functional silos with limited integration between student-facing learning, instructor support, and institutional administrative systems.
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