Improving Learning Efficacy on Duolingo via Generative AI and the Learner Feedback Loop

One of the most challenging aspects of learning a foreign language is learning to converse in that language. While large language models (LLMs) have made it feasible to build generative AI chatbots that simulate conversation, creating an AI-powered language tutor that is both pedagogically effective and engaging for learners requires solving a host of additional problems. At Duolingo, we have developed an AI conversational tutor embedded in our platform, featuring a character named Lily, who helps users practice real-world conversations in their target language. In building this system, we tackled multiple challenges: adapting dialogue to each learner's proficiency level, sustaining personalized and coherent interactions across sessions, maintaining consistent character-driven personality, and designing a structure that supports both guided and learner-initiated topics. Our solution integrates a three-party conversational architecture, a persistent memory mechanism to retain prior interactions, and real-time conversation evaluation to dynamically adjust to the learner's input. This talk will highlight how generative AI, when coupled with rigorous feedback loops and thoughtful design, can significantly enhance the language learning experience.