Pre-training with Scientific Text Improves Educational Question Generation (Student Abstract)

Authors

  • Hamze Muse University College London
  • Sahan Bulathwela University College London
  • Emine Yilmaz University College London

DOI:

https://doi.org/10.1609/aaai.v37i13.27004

Keywords:

Educational Question Generation, Educational Recommenders, Intelligent Tutoring System, Finetuning, Pretraining

Abstract

With the boom of digital educational materials and scalable e-learning systems, the potential for realising AI-assisted personalised learning has skyrocketed. In this landscape, the automatic generation of educational questions will play a key role, enabling scalable self-assessment when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our initial experiments demonstrate that EduQG can produce superior educational questions by pre-training on scientific text.

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Published

2023-09-06

How to Cite

Muse, H., Bulathwela, S., & Yilmaz, E. (2023). Pre-training with Scientific Text Improves Educational Question Generation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16288-16289. https://doi.org/10.1609/aaai.v37i13.27004