Comparing traditional AI, agentic ai and agentic rag for dialogic online education

Abstract

Online education increasingly depends on artificial intelligence (AI) for scale, personalization, and assessment. However, most deployments remain confined to one-shot, content-delivery paradigms that under-serve dialogic pedagogy, an approach centered on multi-voiced inquiry, co-construction of knowledge, and iterative, socially mediated reasoning. This paper synthesizes three paradigms of AI: Traditional AI, Agentic AI, and Agentic Retrieval-Augmented Generation (RAG), and evaluates how each can be applied to online teaching and learning organized around dialogic principles. I articulate a theory-led design space grounded in dialogic pedagogy (Freire, Bakhtin, Wegerif, Alexander) and contemporary learning science (Vygotsky’s ZPD; the Community of Inquiry framework; ICAP). I map each AI paradigm to core online education tasks (tutoring, assessment for learning, discussion orchestration, knowledge building). I propose reference architectures and governance patterns and offer implementation roadmaps, metrics, and risk mitigations. The paper argues that while traditional AI enables efficient, bounded tasks (e.g., automated grading, item generation), agentic AI introduces goal-directed orchestration across tools and actions required for authentic dialogic workflows (e.g., facilitation, critique, reflection). Agentic RAG best aligns with dialogic pedagogy by grounding agent decisions in evolving, cited knowledge; supporting multi-turn planning and verification; and maintaining memory of class discourse and norms. The paper concludes with a pragmatic recommendation: combine Agentic RAG for knowledge-intensive, discourse-heavy learning with narrowly scoped traditional AI services and agentic guards; evaluate with dialogic outcome metrics, not merely accuracy or time-on-task.