12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
← Browse all papers
AI evidence extraction

A Multi-Agent Generative AI System for incorporatingBloom’s Taxonomy, Constructivism, and MetacognitionTheories in Student’s Learning Performance

Khadija Sultana, Omid Ameri Sianaki · Journal of the Association for Information Systems · 2025

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

6/10
Relevance
0/4
Quality (LMQS)
D
Evidence
0
Citations
0.00
FWCI

Methodology & findings

Study design

Design science research with prototype development; creation of a multi-agent system architecture combining theoretical learning frameworks with generative AI implementation

Primary method

Design Science Research

Main result

The study presents a multi-agent architecture that integrates three theory-aligned agents orchestrated to deliver an integrated learning flow. The contribution includes "a conceptual model linking established learning theories to GenAI agent design, (2) a technical architecture for orchestrating multiple theory-driven agents, (3) a validated prompt-engineering framework operationalising educational theory into structured agent behaviours, and (4) a working prototype implemented in Azure AI Foundry."

Research paradigm

Design Science Research / Constructivism

Author conclusions

The authors conclude that their contribution includes "(1) a conceptual model linking established learning theories to GenAI agent design, (2) a technical architecture for orchestrating multiple theory-driven agents, (3) a validated prompt-engineering framework operationalising educational theory into structured agent behaviours, and (4) a working prototype implemented in Azure AI Foundry." This demonstrates that "Generative AI (GenAI) is ubiquitously reshaping higher education" when grounded in pedagogical theory.

Risk of bias

No user study reported in abstract - cannot assess selection bias, participant representativeness, or attrition; Lack of empirical validation limits generalizability claims; Unknown sample size or evaluation methodology

Open questions raised

  • The paper identifies that "Generative AI (GenAI) is ubiquitously reshaping higher education, yet its pedagogical value remains constrained due to lack of theoretical grounding." The gap addressed is that "Current chatbot tutors often operate ad-hoc neglecting established learning principles such as scaffolding, feedback loops, and metacognitive reflection."
  • The paper identifies that "current chatbot tutors often operate ad-hoc neglecting established learning principles such as scaffolding, feedback loops, and metacognitive reflection," positioning the need for theory-grounded generative AI systems.
  • The paper identifies that "Generative AI (GenAI) is ubiquitously reshaping higher education, yet its pedagogical value remains constrained due to lack of theoretical grounding." Future work likely involves validation of the prototype and empirical testing with student populations.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 70%

Explore related topics

AI Literacy TrainingDoctoral TrainingDesign Science

Related papers

← Browse all papers