mlsanityData to 14 Oct 2026

Pre-Training With Scientific Text Improves Educational Question Generation

arXiv 2212.03869 ↗First posted 7 Dec 2022cs.CL cs.AI cs.CYcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 7 Dec 2022.

Builds on

7 of 10 references.

References · 7

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  3. 2017Crowdsourcing Multiple Choice Science Questionscited by 765
  4. 2021BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translationcited by 35
  5. 2019TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resourcescited by 18
  6. 2022Leaf: Multiple-Choice Question Generationcited by 9
  7. 2021Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolutioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.