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.

2024

Investigating student ratings with features of automatically generated questions: A large-scale analysis using data from natural learning contexts

Proceedings of the 17th International Conference on Educational Data Mining

Benny G. Johnson; Jeffrey S. Dittel; Rachel Van Campenhout (2024)

Doer Effect Automatic Question Generation

2024

Exploring large language models for evaluating automatically generated questions

Presented at the Workshop on Automated Evaluation of Learning and Assessment Content, AIED 2024 Workshop

Jeffrey S. Dittel, Michelle W. Clark, Rachel Van Campenhout, Benny G. Johnson (2024)

Automatic Question Generation

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