Explanatory Feedback

Explanatory feedback research focuses on automatically generating helpful and insightful feedback to improve learning and teaching effectiveness across various domains, from educational assessments to professional training. Current research heavily utilizes large language models (LLMs), often employing techniques like retrieval augmented generation, reinforcement learning, and few-shot prompting, to create systems that provide both accurate scoring and detailed, actionable feedback. This work is significant because it addresses the scalability challenges of providing personalized feedback at scale, potentially revolutionizing education, training, and other fields that rely on human expertise for assessment and instruction.