Bulathwela, S;
Muse, H;
Yilmaz, E;
(2023)
Scalable Educational Question Generation with Pre-trained Language Models.
In:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
(pp. pp. 327-339).
Springer Nature
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Abstract
The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale 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 extensive experiments demonstrate that EduQG can produce superior educational questions by further pre-training and fine-tuning a pre-trained language model on the scientific text and science question data.
| Type: | Proceedings paper |
|---|---|
| Title: | Scalable Educational Question Generation with Pre-trained Language Models |
| Event: | Artificial Intelligence in Education 24th International Conference, AIED 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-36272-9_27 |
| Publisher version: | https://doi.org/10.1007/978-3-031-36272-9_27 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10174542 |
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