Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2025

Abstract

Generating high-quality Multiple Choice Questions (MCQs) remains challenging for educational tools due to the need for contextual relevance and plausible distractors. Existing methods still struggle with these dual requirements, leading to questions that lack depth and distractors that are either too obvious or irrelevant. In this paper, we propose BiFlow, a novel framework that integrates bidirectional reasoning perspectives: teacher reasoning generates contextually relevant questions and plausible distractors, while student reasoning evaluates question clarity and the misleading nature of the distractors. To further enhance reasoning, we introduce PathFinder, a mechanism that employs breadth-first search and Chainof-Thought (CoT) strategies to explore diverse reasoning paths, improving both the quality and diversity of generated questions and distractors. Additionally, we enrich the FairytaleQA dataset to FairytaleMCQ with high-quality distractors, providing a robust benchmark for MCQ generation. Experimental results demonstrate that BiFlow outperforms existing methods, particularly in generating text-grounded questions and high-quality distractors for narrative contexts, highlighting its value in educational applications. Project Page can be found here.

Discipline

Artificial Intelligence and Robotics

Areas of Excellence

Digital transformation

Publication

Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1

First Page

8240

Last Page

8253

Identifier

10.18653/v1/2025.findings-acl.432

Publisher

Association for Computational Linguistics

City or Country

USA

Additional URL

https://doi.org/10.18653/v1/2025.findings-acl.432

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