AI evidence extraction

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

Vincent English · Asian Journal of Education and Training · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.20448/edu.v11i4.7926

Methodology & findings

Study design

Theory-led design space analysis and synthesis of three AI paradigms (Traditional AI, Agentic AI, Agentic RAG) mapped to online education tasks grounded in dialogic pedagogy and contemporary learning science.

Main result

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."

Reports effect sizes.

Research paradigm

Interpretivist/Design-oriented

Author conclusions

"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."

Open questions raised

The paper identifies the limitation that most AI deployments in online education "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." It proposes future work in aligning AI with dialogic principles through agentic architectures and dialogic outcome metrics.
The paper identifies the need to move beyond one-shot, content-delivery AI paradigms and address the under-serving of dialogic pedagogy in online education. It calls for evaluation approaches centered on dialogic outcome metrics rather than traditional efficiency measures.
The paper identifies the gap that most AI deployments in online education "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." Future work should empirically validate the proposed Agentic RAG architectures and develop dialogic outcome metrics for assessment.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 69%

Explore related topics

Responsible AI Use Academic Writing Conceptual