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.

本研究设计和构建了一种名为 CORGI 的模型，它可以针对物理控制任务，如学习骑自行车，生成学生的语言纠正，并可以有效提供反馈，促进学生学习。

生成用于教授物理控制任务的语言修正

Generating Language Corrections for Teaching Physical Control Tasks

We present a new method for automatically providing feedback for introductory
programming problems. In order to use this method, we need a reference
implementation of the assignment, and an error model consisting of potential
corrections to errors that students might make. Using this information, the
system automatically derives minimal corrections to student's incorrect
solutions, providing them with a quantifiable measure of exactly how incorrect
a given solution was, as well as feedback about what they did wrong.
We introduce a simple language for describing error models in terms of
correction rules, and formally define a rule-directed translation strategy that
reduces the problem of finding minimal corrections in an incorrect program to
the problem of synthesizing a correct program from a sketch. We have evaluated
our system on thousands of real student attempts obtained from 6.00 and 6.00x.
Our results show that relatively simple error models can correct on average 65%
of all incorrect submissions.

我们提出了一种自动为入门编程问题提供反馈的新方法，该方法利用参考实现的任务和由可能制造的错误组成的错误模型。