Smarter assessment.
Less effort.
Better decisions.
Analyze, diagnose, and transform
Turn your test data into clear, actionable decisions. Test Diagnostics (KR-20/Alpha), Item & Distractor Intelligence, and AI-supported Item ID cards to decide whether to reuse, recycle or upcycle each item.
Validate your assessment scientifically
Run advanced reliability and validity analyses with ease. Select the method, generate results instantly, and interpret them without complexity. Build trust in your assessments with data-driven confidence.
Predict and improve before administration
Predict difficulty and discrimination, estimate CEFR alignment, and identify issues early. Receive AI-powered qualitative and quantitative feedback and suggestions before it reaches your students.
ITEM-042
Distractor Intelligence
GreenLens AI™ Suggestion
This item is too easy and poorly discriminating. Upcycle by revising distractors B & D to be more plausible for high-performing students.
See every item's DNA in seconds.
Every test item gets a full diagnostic card. From psychometric indicators to AI-powered revision suggestions - no item is left behind.
AI rewrites your items. You decide.
Upload your test and GreenLens AI analyzes every stem and distractor. It highlights what changed, why, and lets you accept, reject, or edit each suggestion - full control, zero guesswork.
Options
AI Diagnostics
- Stem contains a proper noun without quotation marks - fixed.
- Option A is too similar to a real-world fact - revised for ambiguity.
- Option C (key) uses vague phrasing - improved specificity.
What gets measured gets improved
Recycling
Don't discard items at the first sign of noise. Diagnose: key issues, wording, distractors, construct mismatch. Then repair and track performance.
Upcycling
Turn weak items into stronger instruments: better stems, sharper distractors, cleaner construct alignment, improved cognitive demand.
Efficiency
Save time and budget by extending item lifespan while protecting validity and reliability. Reduce item waste. Increase evidence.
A sustainable approach to language assessment: treat items as maintainable measurement instruments. Detect what's broken, revise, recalibrate, and reuse-instead of throwing items away.
About Us
Building the future of sustainable language assessment.
Our Mission
Green Testing was founded with a simple but powerful idea: test items are valuable measurement instruments that deserve care, maintenance, and a second life. We believe that with the right psychometric tools, educators can diagnose item weaknesses, repair them, and reuse them - reducing waste and improving assessment quality.
Founders
Dr. Taner Yapar holds a PhD in Educational Measurement and Evaluation. He has been the Head of the Department of Foreign Languages at TOBB University of Economics and Technology (TOBB ETÜ), Ankara, since 2010. His expertise lies in language testing, performance assessment, item response theory and statistics. He is the Joint Coordinator of the Measurement, Evaluation, Testing, and Assessment Special Interest Group (META-SIG) of TESOL Türkiye and a Founding Executive Board Member of DEDAK (Language Education Evaluation and Accreditation Board). Additionally, he is a Certified Cambridge Assessment Trainer and has been conducting language assessment courses in cooperation with ETS in Türkiye.
Ali Emre Karagül is an educational data scientist, instructor, and full-stack developer specializing in psychometrics, statistical modeling, and AI-driven assessment tools. At TOBB ETÜ, he contributes to test development, statistical modeling, automated item generation, automated scoring, and English language instruction. His research focuses on the reliable and validated use of machine learning, natural language processing, and large language models in educational assessment. He has developed automated essay scoring engines, built open-source psychometric analysis tools in R and Python - bridging modern software engineering with educational measurement science and psychometrics.
Contact Us
Have questions or feedback? We'd love to hear from you.