Enterprise AI-Powered Assessment Quality Assurance & Bias Detection System

Core Logic

The Orchestrator Agent coordinates the analysis pipeline by initializing specialist agents, understanding assessment structure, delegating parallel analyses to Quality Analyzer, Bias Auditor, and Alignment Verifier simultaneously, aggregating findings with confidence intervals, facilitating multi-agent consensus for severity classification, and triggering Report Generator for final synthesis. Uses Claude 3 Opus for complex coordination decisions and implements delegation protocols with result aggregation.

Core Logic

The Quality Analyzer Agent performs comprehensive item analysis using both IRT (Rasch model difficulty estimation, discrimination parameters) and CTT (point-biserial correlation, distractor analysis, selection rate patterns). It retrieves psychometric best practices from RAG knowledge base, detects implausible distractors through semantic similarity analysis, identifies ambiguous question stems through linguistic analysis detecting multiple valid interpretations, and generates severity-rated findings with evidence-based recommendations. Each finding includes confidence intervals with epistemic and aleatoric uncertainty quantification.

Core Logic

The Bias Auditor Agent scans question content for bias indicators using a comprehensive taxonomy retrieved from the RAG knowledge base. It detects cultural references specific to certain regions or demographics, identifies linguistic complexity that may disadvantage non-native speakers, flags socioeconomic assumptions about resources or experiences, computes potential impact on affected student populations using institutional demographic data, and generates inclusive alternative wordings. Uses vector similarity matching against known bias patterns and fine-tuned NLP classification for bias type and severity.

Core Logic

The Alignment Verifier Agent generates semantic embeddings for all questions and learning objectives using text-embedding-3-large, computes cosine similarity matrices to identify alignment gaps, detects coverage gaps where objectives have no aligned questions, classifies question cognitive levels using action verb mapping to Bloom's taxonomy, analyzes distribution across Remember/Understand/Apply/Analyze/Evaluate/Create levels, and recommends additions to achieve balanced cognitive level coverage. Retrieves alignment best practices from RAG knowledge base including standards from ACT and curriculum design experts.

Core Logic

The Report Generator Agent synthesizes findings from all specialist agents into comprehensive reports including overall quality scores with component breakdowns (question quality, alignment, fairness, difficulty), risk assessments with factor analysis and mitigation plans, deployment readiness checklists with blocker identification, prioritized recommendations ranked by impact and effort with assigned owners, comparison metrics against institutional and industry benchmarks, and detailed remediation plans with automatable vs manual actions, estimated quality improvement, and side effect analysis. Generates executive summaries for administrators and detailed technical reports for content teams.