AI Detection Reimagined for Integrity

1

Baseline Prompt Analysis

Every student begins with a baseline prompt analyzed by a sentence-level deep-learning classifier that studies syntax rhythm, word frequency, and writing tone to establish a unique authorship fingerprint. This model uses transformer-based encoders similar to those in natural-language understanding systems but fine-tuned for educational authorship detection.

2

Reactive Prompt Verification 

  When a submission is flagged, HonestIQ activates a reactive prompt engine that uses the same neural architecture to compare stylistic and semantic features between the baseline and new writing. This verifies authorship through pattern consistency rather than generic AI probabilities.

3

Authorship Consistency Engine  

The Authorship Consistency Engine (ACE) applies a hybrid of:

- Stylometric Analysis compares sentence structure, lexical diversity, and transitional phrasing.

- Deep-Learning Embedding Models transform each sentence into a numerical signature to detect deviations.

- Keystroke Pattern Analysis (optional) for verified in-person baselines, tracks typing cadence as an additional authorship indicator.

Together, these systems ensure every assignment is judged against the student’s own past work, not a generalized dataset.

 

4​

Citation & Reference Tracker   

 An integrated Citation Tracker scans in-text citations and bibliographies to confirm originality and formatting accuracy, helping educators distinguish between intentional plagiarism and citation errors. This is achieved using a named-entity recognition (NER) layer that detects sources, journals, and reference formats.

 

5

Scoring and Explainability Layer    

Instead of returning a single probability, HonestIQ provides a weighted confidence report, showing how each factor (AI-generated content, plagiarism index, authorship deviation, citation accuracy) contributed to the analysis. This transparency makes HonestIQ’s system explainable, not opaque — an essential factor for fairness and trust.

HonestIQ’s multi-stage detection pipeline ensures every submission is analyzed within context — not isolation.

 

6

 Baseline Prompt Analysis

Each student begins with a baseline writing prompt that captures their natural writing style — sentence flow, punctuation habits, lexical variety, and keystroke rhythm. This becomes their authorship fingerprint.

 

7

Reactive Prompt Verification    

When an assignment is flagged, students can complete a follow-up reactive prompt to verify authorship. This ensures a transparent, educational response rather than a punitive one.

 

8

 Authorship Consistency Engine

The system cross-references past verified submissions with the flagged work, comparing syntax, vocabulary density, sentence structure, and pacing patterns to confirm whether the same student wrote both pieces.

No other detector measures writing in the context of the student’s own history.