Abstract: Apparatuses, systems, and methods are provided for parallel construction for question generation (QG) corresponding to a content item. Text of at least a portion of the content item may be extracted as a source language text, at least a portion of which may be translated to generate a parallel text having a primary language different from the source language. The source and primary language texts may be aligned to create an aligned source language text and an aligned primary language text. A QG strategy may be determined and at least one step to be performed on the aligned primary language text may be generated based at least in part upon the determined QG strategy. At least one parallel operation corresponding to the at least one step to be performed on the aligned primary language text may be constructed, and a source language question may be generated.

Abstract: Described is a method using a computer for grading student work on a problem when the student's own steps are shown in detail. A reference trace is created representing a best solution path to the problem.

Abstract: Described is a method using a computer for grading student work on a problem when the student's own steps are shown in detail. A reference trace is created representing a best solution path to the problem.

Abstract: A question generation means is developed for intelligent tutors wherein a question menu having dynamic questions is capable of being displayed after each step of a problem. The generated question set contains both elementary and advanced questions and changes after each step is performed, thereby enhancing the potential that a student using the tutorial understands every aspect of a problem.

Abstract: A question generation means is developed for intelligent tutors wherein a question menu having dynamic questions is capable of being displayed after each step of a problem. The generated question set contains both elementary and advanced questions and changes after each step is performed, thereby enhancing the potential that a student using the tutorial understands every aspect of a problem.

Abstract: A computerized method for providing a meaningful response in an intelligent tutor. The method includes the steps of receiving an input from a user solving a problem and determining if the input violates at least one consistency rule. The consistency rules are rules implemented to allow a constraint of the input to be evaluated and compared to a fundamental rule set for the problem. The fundamental rule set includes a plurality of principles that impose the constraints on a resulting solution. A student's mistake is applied to the consistency rules to allow the artificial intelligence program to respond to the answer by either providing a necessary principle overlooked by the student, or allows the program to, at a minimum, generate feedback to say something educationally useful to the student. This can be done even when buggy rules have failed to match and provide a rationale for the mistake.

Abstract: A computerized method for providing a meaningful response in an intelligent tutor. The method includes the steps of receiving an input from a user solving a problem and determining if the input violates at least one consistency rule. The consistency rules are rules implemented to allow a constraint of the input to be evaluated and compared to a fundamental rule set for the problem. The fundamental rule set includes a plurality of principles that impose the constraints on a resulting solution. A student's mistake is applied to the consistency rules to allow the artificial intelligence program to respond to the answer by either providing a necessary principle overlooked by the student, or allows the program to, at a minimum, generate feedback to say something educationally useful to the student. This can be done even when buggy rules have failed to match and provide a rationale for the mistake.