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Comparing Traditional AI, Agentic AI and Agentic Rag for Dialogic Online Education

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Title: Comparing Traditional AI, Agentic AI and Agentic Rag for Dialogic Online Education
Language: English
Authors: Vincent English (ORCID 0009-0002-1375-5982)
Source: Asian Journal of Education and Training. 2025 11(4):198-210.
Availability: Asian Online Journal Publishing Group. 244 Fifth Avenue Suite D42, New York, NY 10001. Fax: 212-591-6094; e-mail: info@asianonlinejournals.com; Web site: http://www.asianonlinejournals.com
Peer Reviewed: Y
Page Count: 13
Publication Date: 2025
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Artificial Intelligence, Computer Uses in Education, Electronic Learning, Teaching Methods, Dialogs (Language), Risk, Governance, Evaluation
ISSN: 2519-5387
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 acrosstools 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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1494645
Database: ERIC
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  Data: Comparing Traditional AI, Agentic AI and Agentic Rag for Dialogic Online Education
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  Data: <searchLink fieldCode="SO" term="%22Asian+Journal+of+Education+and+Training%22"><i>Asian Journal of Education and Training</i></searchLink>. 2025 11(4):198-210.
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  Data: Asian Online Journal Publishing Group. 244 Fifth Avenue Suite D42, New York, NY 10001. Fax: 212-591-6094; e-mail: info@asianonlinejournals.com; Web site: http://www.asianonlinejournals.com
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  Data: 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 acrosstools 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.
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      – SubjectFull: Computer Uses in Education
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      – SubjectFull: Electronic Learning
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