Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances MCQ Generation and Distractor Quality (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 Chain-of-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.

## introduction the task of generating multiple choice questions (mcqs) has garnered significant attention due to its vital role in educational tools, assessments, automated evaluation systems and resource generation @xcite @xcite @xcite . over time, question generation has evolved from manual creation by educators @xcite @xcite to being automated through ai-driven systems @xcite @xcite . this integration of ai holds the potential to drastically reduce the time and costs associated with context: lily lost her cat. she searched the park and the town's bakery, where the owner mentioned seeing a white cat a white cat lingering near the entrance. finally, she found snowy sleeping in the backyard of the library. ## fairytalemcq dataset construction the task of multiple choice question (mcq) generation involves creating a question q based on a given context c and a correct answer ans, along with a set of distractors d ′ . these distractors should be plausible yet incorrect to challenge the user's understanding, while ensuring the correct answer ans is the most accurate choice. narrative comprehension, with its intricate storylines and diverse characters, provides a rich context for complex reasoning tasks. however, current narrative comprehension datasets @xcite focus mainly on questionanswer pairs without distractors, making it difficult to evaluate models designed for mcqs generation. at the same time, current mainstream mcq datasets have relatively short contexts, making them unsuitable for complex reasoning questions. to address this gap, we extend the existing fairytaleqa @xcite dataset by generating distractors for each question. this enables a robust evaluation framework for our model. below, we describe the process for constructing the extended dataset. initial dataset. the original fairytaleqa is from real-world reading comprehension corpora. each instance includes a passage of context c, a question q, a correct answer ans, the type of the answer ans_type and an associated question difficulty. distractor generation pipeline. creating high-quality distractors presents a significant challenge, which must meet two essential criteria: 1) deceptiveness: they should be syntactically and semantically similar to the correct answer, making them plausible yet incorrect. 2) consistency: they must not only be contextually relevant but also consistent with the ans_type to ensure alignment with the question's structure and type. to achieve this, we introduce a two-step distractor generation pipeline involving two agents: a distractor generator and a result checker, both implemented using gpt-4. distractor generator d: this agent is responsible for producing three plausible but incorrect distractors based on the context of the question (appendix a.1). these distractors are derived from the context c, ensuring they are syntactically and semantically aligned with the correct answer ans, yet sufficiently misleading to challenge the user's understanding of the material. result checker r: this agent acts as a supervisory layer, ensuring that the distractors generated by d meet specific quality standards . it evaluates the distractors according to three key criteria: syntactic correctness, consistency with ans_type, and plausibility as an incorrect option (appendix a.2). as illustrated in figure 2 , the process is iterative: d generates distractors, and r provides feedback. if any distractor is unsuitable, r suggests revisions. this feedback loop continues until all distractors meet the quality standards. ## method we introduce biflow, a novel framework designed to enhance the generation of mcqs and distractors by integrating dual perspectives of teacher and student reasoning. as shown in figure 3 , biflow, which consists of a question generator ( § 4.1) and a distractor generator ( § 4.2), aims to leverage both the forward-thinking approach of a teacher and the backward reasoning typical of students. ## conclusions in this paper, we introduced biflow, a novel framework for generating high-quality multiplechoice questions (mcqs) and distractors by integrating teacher and student bidirectional reasoning. our approach leverages pathfinder, a mechanism combining breadth-first search and chain-of-thought strategies, to enhance reasoning diversity and quality. experiments on the fairytalemcq dataset demonstrated that biflow significantly out-performed existing methods, particularly in generating complex questions and high-quality distractors for long-text scenarios. the results highlight the effectiveness of our bidirectional reasoning approach in producing pedagogically sound and contextually relevant mcqs.