A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education (2501.05220v1)

Abstract: 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 LLMs like ChatGPT.

• The paper demonstrates that fine-tuning T5 with contrastive examples significantly enhances the topical relevance of generated questions.
• The paper shows that quantisation and data augmentation techniques improve model scalability and efficiency in resource-constrained educational settings.
• The paper highlights that linking Wikipedia concepts to educational content automates question generation, potentially reducing teacher workload.

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

The paper presents a paper on enhancing Automatic Question Generation (QG) models to better serve educational purposes by introducing Topic-Controlled Question Generation (T-CQG). Addressing the persistent issue of high teacher workload, this research proposes a mechanism by which educational content, particularly questions, can be generated automatically while maintaining relevance to the topics being taught.

Methodology

The paper leverages the T5-small model, a pre-trained LLM, fine-tuning it to perform the T-CQG task. The methodology incorporates several data handling strategies, including the novel use of contrastive examples through the creation of unique datasets—SQuAD+, MixSQuAD, and MixSQuAD2X. By linking Wikipedia concepts to contexts and questions, the authors ensured semantic alignment between topics and generated questions.

Several computational experiments were conducted to further the model's capabilities. These included pre-training with a scientific corpus, exploiting model quantisation for improved scalability, and employing data augmentation to boost model robustness.

Findings

The experiments demonstrate that the T-CQG models outperformed the baseline model with notable improvements in metrics assessing linguistic quality and semantic relevance. The findings of the research indicate:

1. Enhanced Topical Relevance: The fine-tuned models were effective at generating questions that aligned well with the given topical contexts. Semantic relatedness metrics validated the improvements in topic-specific question generation.
2. Scalability and Efficiency: Through post-training quantisation methods, the memory footprint of the models was reduced significantly, facilitating deployment in resource-constrained environments without significant loss of performance. This offers a sustainable approach to implementing AI solutions in education sectors with limited infrastructure.
3. Model Generalization via Data Augmentation: The use of data augmentation by reversing context concatenations helped improve the model's ability to generalize and produce educationally meaningful, topic-aligned questions.

By addressing the challenge of producing semantically relevant questions tied to specific topics, the authors propose a method with practical implications. The method presents a model that can be feasibly integrated into educational technologies, aiming to reduce teacher workload while supporting personalized learning needs in a scalable, accessible, and cost-effective manner.

Implications and Future Directions

This research holds significant implications for AI's role in education. The potential for reduced teacher workload and improved student assessment through topic-specific question generation is considerable. From practical integration in learning management systems (LMSs) and intelligent tutoring systems (ITSs) to fostering a more personalized education framework, the avenues for impact are diverse.

Future research could expand the current framework by incorporating additional aspects of educational content generation, such as feedback and explanatory context, to further tailor the learning experience. Additional investigations into multilingual settings and domain-specific adaptations can extend the applicability of T-CQG models. Moreover, improved methods for evaluating generated content quality will enhance alignment with pedagogical goals.

Overall, this solution offers a promising addition to the suite of AI tools poised to transform educational practices and support teachers worldwide.