  The increase in academic dishonesty cases among college students has raised
concern, particularly due to the shift towards online learning caused by the
pandemic. We aim to develop and implement a method capable of generating
tailored questions for each student. The use of Automatic Question Generation
(AQG) is a possible solution. Previous studies have investigated AQG frameworks
in education, which include validity, user-defined difficulty, and personalized
problem generation. Our new AQG approach produces logical equivalence problems
for Discrete Mathematics, which is a core course for year-one computer science
students. This approach utilizes a syntactic grammar and a semantic attribute
system through top-down parsing and syntax tree transformations. Our
experiments show that the difficulty level of questions generated by our AQG
approach is similar to the questions presented to students in the textbook [1].
These results confirm the practicality of our AQG approach for automated
question generation in education, with the potential to significantly enhance
learning experiences.
