About SafeAssign AI Detector
We build tools that help students understand how AI detection works — before Blackboard does.
Why We Built This Tool
SafeAssign AI Detector was created by a team of academic technology researchers who noticed a consistent gap: students submitting work through Blackboard had no way to preview how SafeAssign’s AI detection layer would evaluate their writing. They only found out after submitting — when it was too late to revise.
Our goal is to close that gap. By simulating the same linguistic and statistical pattern analysis that SafeAssign runs on every submission, we give students a transparent preview of their AI detection risk before they upload anything to Blackboard.
We believe academic integrity starts with understanding the tools that evaluate your work. This site is not a workaround — it is a diagnostic. We built it so students can submit with confidence, knowing exactly where their writing stands.
Our Testing Methodology
Corpus Collection
We analyzed over 50,000 student submissions across undergraduate and graduate levels, comparing SafeAssign-flagged papers against confirmed human-written samples to identify detection patterns.
Signal Extraction
From this corpus, we extracted 14 quantifiable linguistic signals — including sentence entropy, lexical diversity index, syntactic predictability, and transition phrase frequency — that correlate with high SafeAssign AI scores.
Model Calibration
Our scoring model was calibrated against 3,200 real SafeAssign reports to minimize variance between our predicted score and the actual score students received. Current calibration accuracy sits at ±6 percentage points.
Continuous Validation
We update our model quarterly as SafeAssign’s detection algorithm evolves. Validation runs are performed against a held-out test set of 400 submissions not used in training.
Who We Are
Marcus Reid
Former academic integrity officer with 9 years of experience evaluating AI detection tools across 4 university systems.
Sophia Park
Computational linguist specializing in stylometric analysis and automated text classification for academic environments.
Daniel Voss
Responsible for corpus analysis, signal extraction, and quarterly model recalibration against live SafeAssign output data.
Aisha Laurent
Works directly with student communities to gather feedback and ensure the tool addresses real pre-submission anxiety.
SafeAssign AI Detector is an independent research tool. This site is not affiliated with Blackboard Inc. or SafeAssign in any way. All trademarks belong to their respective owners.
Results produced by this tool are estimates based on statistical modeling and should not be treated as a guarantee of any actual SafeAssign score.