EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research
Chenguang Pan, Zhou Zhang, Weixuan Xiao, Chengyuan Yao

TL;DR
EDM-ARS is an innovative multi-agent system that automates end-to-end educational data mining research, generating complete scholarly articles with embedded domain expertise and automated peer review.
Contribution
The paper introduces EDM-ARS, a novel domain-specific multi-agent framework that automates educational data mining research from data to publication.
Findings
Automates end-to-end EDM research process
Generates complete LaTeX manuscripts with citations
Supports revision, recovery, and self-correction mechanisms
Abstract
In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware automated research pipelines, where educational expertise is embedded into each stage of the research lifecycle. As a first instantiation of this framework, we focus on predictive modeling tasks. Within this scope, EDM-ARS orchestrates five specialized LLM-powered agents (ProblemFormulator, DataEngineer, Analyst, Critic, and Writer) through a state-machine coordinator that supports revision loops, checkpoint-based recovery, and sandboxed code execution. Given a research prompt and a dataset, EDM-ARS produces a complete LaTeX manuscript with real Semantic Scholar citations, validated machine learning analyses, and…
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Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Topic Modeling
