These production rules are called “buggy rules”.

这些产生式规则称为“错误规则”。

The knowledge required to do this step can be represented by something called a production rule.

执行此步骤所需的知识可以用称为产生式规则的东西表示。

So, production rules are combined with an algorithm that selects the most likely one.

因此，生产规则与选择最有可能的算法相结合。

The cool thing about production rules is that they can also be used to represent common mistakes a student might make.

产生式规则的妙处在于它们还可以用来表示学生可能犯的常见错误。

The pseudo code of a production rule for this step would say if there is a constant on the same side as the variable, then subtract that constant from both sides.

这一步的产生式规则的伪代码会说如果在变量的同一侧有一个常量，则从两边减去该常量。

It’s totally possible that multiple competing production rules are triggered after a student completes a step – it may not be entirely clear what misconception has led to a student’s answer.

学生完成一个步骤后，完全有可能触发多个相互竞争的产生式规则——可能并不完全清楚是什么误解导致了学生的回答。

For that, intelligent tutoring systems build and maintain a student model – one that tracks, among other things, what production rules a student has mastered, and where they still need practice.

为此，智能辅导系统建立并维护一个学生模型——一个跟踪学生掌握了哪些生产规则，以及他们还需要练习的地方。

These production rules, and the selection algorithm, combine to form what’s called a Domain Model, which is a formal representation of the knowledge, procedures and skills of a particular discipline - like algebra.

这些产生式规则和选择算法结合起来形成了所谓的领域模型，它是特定学科（如代数）的知识、程序和技能的形式化表示。