# LLM Integration in Major EdTech Platforms ## Overview This case study examines how three major EdTech companies - Duolingo, Brainly, and SoloLearn - have implemented and operationalized LLMs in their production environments. Each company serves hundreds of millions of learners globally and has taken different approaches to integrating AI into their educational products. ## Company Profiles and Use Cases ### Duolingo - Primary focus on language learning, with recent expansion into math and music - Long history of AI usage (around a decade) starting with personalization - Key LLM applications: - Scale considerations: ### Brainly - Community learning platform focusing on homework help across all school subjects - Serves hundreds of millions of learners monthly - Key LLM implementations: - Implementation approach: ### SoloLearn - Mobile-first coding education platform - 30+ million registered users - Focus areas: ## Key Technical Challenges and Solutions ### Fact Generation and Accuracy - Raw LLM outputs often unreliable for educational content - Solutions implemented: ### Cost Management - Large-scale deployment challenges with inference costs - Strategies employed: ### Content Personalization - Challenge of matching correct difficulty levels - Solutions: ## Production Best Practices ### Controlled Rollout - Start with heavily constrained features - Gradual expansion based on performance data - Careful monitoring of accuracy and user feedback ### Quality Control - Multiple layers of verification - Integration of expert feedback - Continuous evaluation of model outputs - Strong focus on educational effectiveness ### Scale Considerations - Careful balance between feature capability and cost - Infrastructure optimization for large-scale deployment - Efficient use of computational resources ## Lessons Learned ### What Works - Using LLMs for synthesis rather than pure fact generation - Augmenting prompts with verified information - Focusing on teaching effectiveness rather than just content generation - Pre-generating content for cost-effective scaling - Building specialized models for specific educational tasks ### What Doesn't Work - Relying on raw LLM outputs without verification - Generic chatbot approaches without educational design - Assuming LLMs inherently understand how to teach effectively - Scaling expensive inference operations without optimization ## Future Directions ### Interactive Learning - Development of more sophisticated interactive experiences - Better personalization of learning paths - Improved adaptation to learner needs ### Educational AI Integration - Growing acceptance of AI in educational settings - Potential solutions for educational system challenges - Focus on making complex subjects more accessible ### Technical Evolution - Continued improvement in model efficiency - Better tools for educational content generation - More sophisticated personalization capabilities ## Conclusion The experiences of these major EdTech platforms demonstrate both the potential and challenges of implementing LLMs in production educational environments. Success requires careful attention to accuracy, cost management, and educational effectiveness, while maintaining a focus on actual learning outcomes rather than just technical capabilities.