A new AI system, RELEX, uses personalized example retrieval powered by a Large Language Model to significantly improve learners' procedural writing skills, demonstrated through cooking recipes.

In plain terms

Writing clear 'how-to' instructions, known as procedural writing, is often challenging for learners. Traditional example-based learning, which provides feedback through examples, can be ineffective when everyone receives the same content, regardless of their individual needs. To tackle this, researchers developed RELEX, an adaptive learning system designed to personalize example-based feedback. The system first uses a fine-tuned Large Language Model (an advanced AI for text generation) to predict the quality of a learner's cooking recipe. Based on this quality score, RELEX then retrieves a higher-quality and contextually similar example recipe from a vast database, enriching it with personalized instructional explanations. A study with 200 participants showed that providing these tailored examples led to better writing performance and a more positive user experience.