Data AnalysisAugments Human LaborVerified
Reviewed and published by trentmaziarz, March 21, 2026. Discovered and drafted by our automated research pipeline.
When someone takes the Duolingo English Test at home, an AI system monitors the session by analyzing video, audio, typing patterns, and eye movements — looking for signs of rule violations across more than 75 different behavioral and environmental signals. AI flags potential issues, and then human proctors make the final call on whether to certify the test.
Details
Each Duolingo English Test session is recorded through the test-taker's webcam, microphone, keyboard, and mouse. AI algorithms — using computer vision (analyzing what the camera sees) and natural language processing (analyzing response patterns) — review the session first, identifying potential violations. Human proctors then independently review flagged sessions and conduct their own checks. The full review process completes within 48 hours of test submission. Duolingo states that AI's role in proctoring is to assist human proctors rather than replace them. A peer-reviewed study on the system's validity and fairness was published in Educational Measurement, Issues and Practice (Wiley).
Products affected
Duolingo English Test
Sources & Evidence
Company Disclosure
Other practices by Duolingo
Customer SvcDuolingo uses an AI chatbot (powered by a company called Decagon) to handle the majority of customer support requests for the Duolingo English Test. The AI fully resolves about 80% of chat inquiries without any human involvement, answering questions about test registration, scores, and institutional acceptance.Data AnalysisDuolingo uses a machine learning model to decide, in real time, whether to show each individual user an advertisement, a subscription offer, or neither — predicting which choice is most likely to generate revenue from that specific person at that moment. The system was credited with generating tens of millions of dollars in additional annual revenue.Data AnalysisDuolingo uses a machine learning algorithm to decide which push notification to send each user, at what time, and how often — optimizing for the messages most likely to get a lapsed learner back into the app. The system was published as research in 2020 and was shown to increase the number of daily active users by 0.5% and improve retention of new users by 2%.
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