Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To address these challenges, we introduce GELEX (GEnerative-AI Learning through EXamples), a hybrid Artificial Intelligence (AI) system enhancing example-based learning by using large language models (LLMs). Our hybrid system incorporates mechanisms to control and evaluate the AI output, acknowledging and addressing the potential factual inaccuracies of LLMs. We instantiate our system in the cooking domain. Our approach utilizes association rule mining on a large database of recipes to identify key patterns. When learners submit a recipe for feedback, a LLM enriches it by integrating these patterns. Then, learners are prompted to actively process the example by highlighting the changes and critically assessing the modifications. This strategy transforms traditional example-based learning into a dynamic, scalable, interactive educational tool.