Automatic Question Generation

Automatic question generation allows us to scale the Doer Effect.

Using artificial intelligence methods, we can use textbook content to generate questions, and these questions can be created for textbooks nearly instantaneously to provide students with the learning by doing practice they need. However, it’s crucial to know that these questions are good. Our award winning research analyzed engagement, difficulty, and persistence of automatically generated questions and human authored questions answered by the same students in the same courseware. This research has shown:

Students do not treat automatically generated questions differently than human authored questions.
The differences in engagement, difficulty, and persistence were actually related to the cognitive process dimension of the question (recognition versus recall), not how the questions were created.
Easy questions did not deter students from answering, but very difficult questions did decrease student persistence in answering.

Recent Publications About Automatic Question Generation

Search our catalog of recent publications authored by our team.

2025

AI principles in practice with a learning engineering framework

The 17th International Conference on Computer Supported Education

Rachel Van Campenhout, Nick Brown, and Benny G. Johnson (2025)

Automatic Question Generation

2025

Intrinsic and contextual factors impacting student ratings of automatically generated questions: A large-scale data analysis

The Journal of Educational Data Mining

Jeffrey S. Dittel, Benny G. Johnson, and Rachel Van Campenhout (2025)

Automatic Question Generation

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