# A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education

arXiv: https://arxiv.org/abs/2501.05220 · HTML: https://mlsanity.com/paper/2501.05220/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 21 of 21)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://mlsanity.com/paper/2208.07339/) (2022)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [A Distributional Approach to Controlled Text Generation](https://mlsanity.com/paper/2012.11635/) (2020)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree?](https://mlsanity.com/paper/2303.11146/) (2023)
- [Learning gain differences between ChatGPT and human tutor generated algebra hints](https://mlsanity.com/paper/2302.06871/) (2023)
- [On the application of Large Language Models for language teaching and assessment technology](https://mlsanity.com/paper/2307.08393/) (2023)
- [How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes](https://mlsanity.com/paper/2401.05914/) (2024)
- [TextDescriptives: A Python package for calculating a large variety of metrics from text](https://mlsanity.com/paper/2301.02057/) (2023)
- [Simplifying Paragraph-level Question Generation via Transformer Language Models](https://mlsanity.com/paper/2005.01107/) (2020)
- [Large Language Models for Education: Grading Open-Ended Questions Using ChatGPT](https://mlsanity.com/paper/2307.16696/) (2023)
- [Adoption of Artificial Intelligence in Schools: Unveiling Factors Influencing Teachers Engagement](https://mlsanity.com/paper/2304.00903/) (2023)
- [A Toolbox for Modelling Engagement with Educational Videos](https://mlsanity.com/paper/2401.05424/) (2023)

## Built on by (most cited first, 11 of 11)

- [Multi-Agent Collaborative Framework For Math Problem Generation](https://mlsanity.com/paper/2511.03958/) (2025)
- [Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour](https://mlsanity.com/paper/2511.02599/) (2025)
- [Rethinking the Potential of Multimodality in Collaborative Problem Solving Diagnosis with Large Language Models](https://mlsanity.com/paper/2504.15093/) (2025)
- [Enhancing Diversity of LLM-Generated Educational Tasks](https://mlsanity.com/paper/2512.23601/) (2025)
- [Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos](https://mlsanity.com/paper/2505.01790/) (2025)
- [Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models](https://mlsanity.com/paper/2512.10110/) (2025)
- [Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning](https://mlsanity.com/paper/2605.23925/) (2026)
- [Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence](https://mlsanity.com/paper/2511.19580/) (2025)
- [Personalized AI Practice Replicates Learning Rate Regularity at Scale](https://mlsanity.com/paper/2604.03246/) (2026)
- [Mix and Match: Context Pairing for Scalable Topic-Controlled Educational Summarisation](https://mlsanity.com/paper/2604.18087/) (2026)
- [KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing](https://mlsanity.com/paper/2605.23933/) (2026)

## Abstract (arXiv)

The development of Automatic Question Generation (QG) models has the potential to significantly improve educational practices by reducing the teacher workload associated with creating educational content. This paper introduces a novel approach to educational question generation that controls the topical focus of questions. The proposed Topic-Controlled Question Generation (T-CQG) method enhances the relevance and effectiveness of the generated content for educational purposes. Our approach uses fine-tuning on a pre-trained T5-small model, employing specially created datasets tailored to educational needs. The research further explores the impacts of pre-training strategies, quantisation, and data augmentation on the model's performance. We specifically address the challenge of generating semantically aligned questions with paragraph-level contexts, thereby improving the topic specificity of the generated questions. In addition, we introduce and explore novel evaluation methods to assess the topical relatedness of the generated questions. Our results, validated through rigorous offline and human-backed evaluations, demonstrate that the proposed models effectively generate high-quality, topic-focused questions. These models have the potential to reduce teacher workload and support personalised tutoring systems by serving as bespoke question generators. With its relatively small number of parameters, the proposals not only advance the capabilities of question generation models for handling specific educational topics but also offer a scalable solution that reduces infrastructure costs. This scalability makes them feasible for widespread use in education without reliance on proprietary large language models like ChatGPT.