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Built for Integrity. Powered by Verification

HonestIQ is not a single detection tool.

It is a multi-layered academic integrity system designed to analyze, verify, and validate both written and quantitative work.

From authorship to mathematical reasoning, every submission is evaluated through structured intelligence and human-led decision making. 

A Multi-Layer Integrity Architecture


HonestIQ operates through three core layers:

Detection Layer


Identifies integrity signals across qualitative and quantitative submissions

  1. AI-generated content patterns

  2. Plagiarism indicators

  3. Structural anomalies in writing

  4. Solution path deviations in mathematical work

  5. Step-by-step reasoning inconsistencies

  6. Unexpected complexity or logic shifts

Profile Layer


Builds student-specific intelligence over time

  1. Writing DNA - AIE
  2. Math Profile - MEI
  3. Behavioral consistency patterns
  4. Baseline performance models

Verification Layer


Signals are not treated as conclusions they are validated through verification workflows.

Quantitative Verification - QCV


Validates mathematical understanding
  1. Targeted follow-up problems
  2. Confirms true problem-solving ability
  3. Detects inflated or AI-assisted reasoning
  4. Eliminates false positives in quantitative work

Qualitative Verification - QICV


Validates authorship and written reasoning
  1. Structured clarification prompts
  2. Writing consistency validation
  3. Authorship confirmation workflows
  4. Supports dispute resolution and appeals

Dual Intelligence Engines

Authorship Intelligence -AIE


Analyzes written work beyond surface-level detection

  • Writing DNA modeling
  • Stylometric analysis
  • Semantic structure evaluation
  • Authorship consistency tracking

Mathematical Intelligence -MEI


Analyzes how solutions are built—not just answers

  • Solution path analysis
  • Step-by-step reasoning evaluation
  • Entropy and complexity modeling
  • Mathematical behavior tracking

Institutional Integrity Framework


The Governance Engine Behind HonestIQ

HonestIQ extends beyond analysis through a structured institutional framework that governs how integrity is reviewed, verified, and resolved.

This ensures every signal is handled through a controlled, fair, and auditable process.

Governed Integrity Workflows


  1. Instructor-led review and decision making
  2. Structured escalation pathways
  3. No automated enforcement or penalties
  4. Role-based access across instructors, admins, and committees

Verification-Driven Resolution


  1. Integrated QCV (Quantitative Verification)

  2. Integrated QICV (Qualitative Verification)

  3. Evidence-based review processes

  4. Standardized case handling

Dispute & Appeals System


  1. Student clarification workflows

  2. Formal appeal submission process

  3. Integrity Committee review (multi-role governance)

  4. Resolution tracking and documentation

When clarification requires deeper validation, structured oral defense is initiated.

Oral Defense - Virtual Verification


  1. Instructor-approved, scheduled verification session
  2. Students demonstrate reasoning in real time
  3. May be requested by students to support clarification or appeal, subject to approval
  4. Applies to both mathematical and written submissions
  5. Fully recorded and audit-tracked
  6. Integrated into the formal dispute and resolution process

Audit & Transparency


  1. Full audit trail of all actions

  2. Case-level history and decision logs

  3. Institutional reporting and analytics

  4. Compliance-ready documentation

From Signal to Decision


HonestIQ does not automate consequences.

All signals pass through a controlled decision system:

  1. Instructor-led validation

  2. No automatic student-facing action

  3. Structured decision pathways

  4. Full audit trail and transparency

Instructor Decision Gate

All integrity signals remain internal until reviewed.

No student-facing action occurs without instructor confirmation.


Instructor action is required:`

  1. Confirm Integrity Concern → Flag Activated
  2. Dismiss Concern → No Action Taken
  3. Request Clarification → Student Verification Triggered

All actions are logged, traceable, and governed by institutional policy.

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Why This Matters


Traditional systems detect.

  1. Reduces false positives
  2. Protects students from wrongful flags
  3. Provides real evidence—not assumptions
  4. Ensures fairness across institutions

Start Pilot Access Explore AIE in Action Explore MEI in Action