AI detector for universities

Our models are trained on academic-style writing and coursework, not just marketing or web content. This reduces false positives for legitimate student writing while maintaining high sensitivity to AI-generated text. See how teachers use Pangram for assignment verification.

We provide granular, sentence- and paragraph-level highlighting that distinguishes AI-generated sections from human-written text. This allows faculty to make nuanced judgments rather than relying on binary pass/fail determinations, especially important for multi-chapter academic work.

Yes. Pangram supports detection across technical, analytical, and narrative writing styles, making it suitable for everything from lab reports and coding assignments to essays, literature reviews, and policy papers.

Yes. Pangram adheres to FERPA-aligned data privacy and handling standards required by U.S. educational institutions. Student submissions are processed securely and are not used to train external models.

Universities can configure data retention policies based on institutional requirements. Data can be automatically deleted after analysis or retained for audit and academic integrity review workflows.

Yes. Many institutions use Pangram not only for detection, but also to operationalize AI policies by defining acceptable vs. unacceptable use cases and applying consistent review standards across departments.

No. Pangram is designed as a decision-support tool, not an enforcement mechanism. Results are presented with confidence scores and explanations so faculty and academic integrity committees can make informed determinations.

Yes. Many universities use our high-throughput API to analyze AI usage trends across large datasets, enabling research into academic integrity, AI adoption patterns, and pedagogical impact.

Yes. Administrators can aggregate anonymized insights across courses, departments, or semesters to understand how generative AI is affecting learning outcomes and assessment strategies.

Pangram supports integrations and workflows compatible with common LMS environments including Canvas, Blackboard, Moodle, and Brightspace, making it easy to incorporate AI detection into existing assignment submission and review processes.

Yes. Many institutions choose to share detection insights with students as part of an educational or corrective process, helping reinforce responsible AI usage rather than relying solely on punitive measures.

The system emphasizes explainability and transparency. Highlighted sections, confidence scoring, and contextual signals help reduce over-reliance on a single metric and support fair academic review.