Multi-Level Bad Pair/ Enemy Detection

We built and enabled our enemy detection feature, which helps us automatically identify and restrict items that could compromise test quality if used together. We use this to prevent overlaps or shared cues that might unintentionally reveal answers. We also watch for similar wording or redundant content that could confuse test-takers, distort measurement of learning objectives, or disrupt the balance and structure defined in our test blueprints.

By proactively managing these item relationships, we ensure our assessments remain valid, reliable, and well-constructed.

We actively detect multi-level bad pairs—items that appear across different sections, levels, or forms of a test—so we can prevent issues like one item at Level 1 giving away clues to another at Level 2, or unintentional repetition across modules. We make it our priority to maintain item independence and reduce redundancy in our assessments. Our team is committed to preventing content leakage and cues between sections. With our enemy detection feature, we strengthen the integrity of our adaptive testing models and ensure a fair, balanced testing experience for all.

We identify conflicting or redundant items through content- and context-aware algorithms powered by NLP, helping us avoid cognitive dissonance and preserve the integrity of our item pool. This ensures each assessment remains clear, consistent, and aligned with its intended learning outcomes.