Muse, H;
Bulathwela, S;
Yilmaz, E;
(2023)
Pre-training with Scientific Text Improves Educational Question Generation.
In:
Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023.
(pp. pp. 16288-16289).
Association for the Advancement of Artificial Intelligence (AAAI)
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Pre-training with Scientific Text Improves Educational Question Generation |
| Event: | Thirty-Seventh AAAI Conference on Artificial Intelligence Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence |
| ISBN-13: | 9781577358800 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ojs.aaai.org/index.php/AAAI/issue/view/560 |
| 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.ucl.ac.uk/id/eprint/10175833 |
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