AI assistance continues to help advance applications in education, from language learning to intelligent tutoring systems, yet current methods for providing students feedback are still quite limited. Most automatic feedback systems either provide binary correctness feedback, which may not help a student understand how to improve, or require hand-coding feedback templates, which may not generalize to new domains. This can be particularly challenging for physical control tasks, where the rich diversity in student behavior and specialized domains make it challenging to leverage general-purpose assistive tools for providing feedback. We design and build CORGI, a model trained to generate language corrections for physical control tasks, such as learning to ride a bike. CORGI takes in as input a pair of student and expert trajectories, and then generates natural language corrections to help the student improve. We collect and train CORGI over data from three diverse physical control tasks (drawing, steering, and joint movement). Through both automatic and human evaluations, we show that CORGI can (i) generate valid feedback for novel student trajectories, (ii) outperform baselines on domains with novel control dynamics, and (iii) improve student learning in an interactive drawing task.

翻译：人工智能辅助持续推动教育领域应用的发展，从语言学习到智能辅导系统，但当前为学生提供反馈的方法仍相当有限。大多数自动反馈系统要么仅提供正确/错误的二元反馈（这无助于学生理解如何改进），要么需要手工编码反馈模板（这难以泛化到新领域）。这一挑战在物理控制任务中尤为突出：学生行为的丰富多样性和专业领域的特殊性，使得利用通用辅助工具提供反馈变得困难。我们设计并构建了CORGI模型，该模型专为物理控制任务（如学习骑自行车）生成语言纠错反馈。CORGI以学生与专家轨迹对作为输入，生成自然语言纠错指导以帮助学生改进。我们通过三个不同物理控制任务（绘图、转向、关节运动）的数据集训练CORGI。自动评估与人工评估表明，CORGI不仅能针对新颖学生轨迹生成有效反馈，在具有新型控制动态的领域中也优于基线方法，并且能在交互式绘图任务中有效提升学生学习效果。