A Multi-Agent AI System for Automated High School Transcript Processing: Collaborative Document Analysis at Scale
Quick Answer
This paper shows that A multi-agent AI system automates high school transcript processing, achieving 96.7% accuracy on 40 transcripts with a processing time of 45 seconds each.
Quick Take
A multi-agent AI system automates high school transcript processing, achieving 96.7% accuracy on 40 transcripts with a processing time of 45 seconds each. This solution addresses operational bottlenecks in college admissions by employing specialized agents for parsing, semantic analysis, and document analysis, coordinated by an Orchestration Agent.
Key Points
- The system processes diverse transcript formats using specialized agents.
- Achieved 96.7% accuracy compared to expert manual reviews.
- Processing time is reduced to 45 seconds per transcript.
- Utilizes agent-based quality control for reliable collaboration.
- Scalable solution for college admissions offices facing high volumes.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 13916v1 Announce Type: new Abstract: Each year, college admissions offices face an overwhelming challenge: processing millions of high school transcripts, each with unique formats, grading systems, and layouts. This manual process creates operational bottlenecks that delay admissions decisions and consume valuable resources.
We present a transformative solution through a multi-agent AI system where specialized agents collaborate to automatically process diverse transcript formats through intelligent coordination and communication.
Our multi-agent architecture consists of three specialized agents-a Pattern Recognition Agent for format-specific parsing, a Semantic Analysis Agent for natural language understanding, and a Vision Intelligence Agent for multimodal document analysis-coordinated by an Orchestration Agent that manages agent communication and result reconciliation.
Our key innovation lies in agent-based quality control using GPA extraction as a coordination signal, ensuring reliable agent collaboration and preventing critical information loss. When evaluated on 40 real world transcripts from high schools across 13 U. S. states, our agent system successfully processed every document, achieving 96. 7% accuracy compared to expert manual review while maintaining practical processing speeds of 45 seconds per transcript.
This work demonstrates how multi-agent coordination can solve complex document processing challenges, offering institutions a scalable, collaborative AI solution that preserves accuracy while dramatically reducing processing time.
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