Automated Contract Cheating Investigation Workflow

Contract cheating investigation is a manual, time-intensive detective process for faculty and administrators, often relying on inconsistent gut-checks and fragmented data. A custom automation workflow replaces this with a systematic, multi-agent architecture. It ingests submissions, analyzes metadata, writing style drift, and external source patterns to build investigatory cases, flagging high-risk submissions for review. This reduces investigative workload by 80-90% and creates a defensible, auditable process that scales across large cohorts.

Implementation integrates directly with LMS platforms like Canvas or Moodle via API. The orchestration layer, built with LangGraph, coordinates specialized agents for discrete analysis tasks, each returning scored evidence. A central case builder synthesizes signals into a risk-scored report, routed to a human review dashboard in systems like Salesforce Service Cloud. Controls include confidence thresholds, mandatory human review for high-risk cases, and full audit trails to support academic judiciary proceedings, ensuring governance and policy adherence.

This workflow automates the forensic analysis of student submissions to identify contract cheating, where work is outsourced to third parties. It eliminates the manual detective work of comparing writing styles, checking submission metadata, and cross-referencing external sources. The operational upside comes from reducing faculty investigation time by 80-90%, allowing institutions to scale integrity enforcement across large cohorts without proportional administrative burden, thereby protecting academic standards and institutional reputation.

Implementation integrates with LMS platforms like Canvas or Moodle via webhook triggers. The orchestrator, built with LangGraph, coordinates specialized agents that query internal SIS data, analyze writing style vectors over time, and scan external essay mills. A key constraint is data quality and establishing a robust baseline for stylometric comparison. The architecture includes approval gates where high-confidence cases auto-generate a report for an academic review board, while low-confidence findings are queued for instructor review, ensuring governance and reducing false positives.

This workflow automates the labor-intensive investigation of contract cheating, where students outsource assignments. It ingests submissions, analyzes metadata, writing style drift, and external source patterns to build investigatory cases. The operational upside comes from reducing faculty detective work by 80-90%, allowing them to focus on high-confidence reviews. Savings are realized through scalable oversight, consistent policy application, and reduced risk of undetected academic misconduct across large cohorts.

Implementation requires phased integration with the LMS (Canvas, Moodle), SIS (Banner, Workday), and a vector database for historical writing samples. The orchestration layer, built with LangGraph, sequences specialized agents for discrete analysis tasks. Critical controls include configurable confidence thresholds, mandatory human review for high-stakes cases, and a full audit trail of all agent reasoning and evidence for defensible disciplinary proceedings. Rollout starts with a pilot course to tune detection logic before scaling.

Contract cheating investigation is a manual, time-intensive detective task for faculty and administrators. A custom workflow automates this by orchestrating specialized agents to analyze submission metadata, writing style drift, and external source patterns against a student's historical work. This creates a preliminary evidence package, flagging high-risk cases for review. The operational upside is a 70-80% reduction in manual investigation time, allowing staff to focus on adjudication rather than evidence gathering, while creating a consistent, defensible process.

Implementation integrates with LMS APIs (Canvas, Moodle, Blackboard) and document stores. The orchestrator, built with LangGraph or a custom microservice, manages agent execution, evidence fusion, and logging. Critical controls include configurable confidence thresholds, mandatory human review for high-stakes cases, and immutable audit trails linking all agent reasoning to the final dossier. Rollout requires phased testing with historical cases to calibrate detection sensitivity and exception routing before full production deployment.

Effective investigation hinges on ingesting and correlating disparate data streams. The workflow must pull submission metadata (timestamps, IP), access logs from the LMS (Canvas, Moodle), historical student writing samples, and external source repositories. Data quality and lineage are paramount; missing or corrupted logs can break the evidentiary chain. Orchestrators like LangGraph manage these parallel ingestion tasks, normalizing data into a unified case file before analysis begins, ensuring all agents operate on a consistent, timestamped record.

Model selection dictates investigative precision. A multi-agent system employs specialized models: one for stylometric analysis to detect authorship drift, another for semantic search against essay mills and homework sites, and a third to correlate behavioral anomalies from proctoring data. These models must output explainable confidence scores and supporting evidence snippets. Integration points are critical; the final dossier must publish seamlessly to the Student Information System (SIS) like Workday or Banner for review, with all logic and data changes logged for academic integrity hearings and potential appeals.