Beyond the Textual: Generating Coherent Visual Options for MCQ s (2025)

Abstract: Multiple-choice questions (MCQs) play a crucial role in fostering deep thinking and knowledge integration in education. However, previous research has primarily focused on generating MCQs with textual options, but it largely overlooks the visual options. Moreover, generating high-quality distractors remains a major challenge due to the high cost and limited scalability of manual authoring. To tackle these problems, we propose a Cross-modal Options Synthesis (CmOS), a novel framework for generating educational MCQs with visual options. Our framework integrates Multimodal Chain-of-Thought (MCoT) reasoning process and Retrieval-Augmented Generation (RAG) to produce semantically plausible and visually similar answer and distractor. It also includes a discrimination module to identify content suitable for visual options. Experimental results on test tasks demonstrate the superiority of CmOS in content discrimination, question generation and visual option generation over existing methods across various subjects and educational levels.

## introduction in the field of education, multiple-choice questions (mcqs) play a crucial role in promoting deep thinking and knowledge integration. prior studies have shown that well-written mcqs can support learner engagement in higher levels of cognitive reasoning such as application or synthesis of knowledge @xcite . among the components of mcqs, the difficulty and relevance of distractors are key indicators of question quality @xcite . however, systematically crafting quality assessment questions and distractors in education is a crucial yet time-consuming, expertise-driven undertaking that calls for innovative solutions @xcite . with the rapid advancement of large language models (llms) that demonstrate remarkable capa-* corresponding author. email: helongzhu@bupt.edu.cn bilities in scientific question answering, researchers have begun to explore their potential in the automatic generation of educational questions to alleviate human labor, including objective and openended formats @xcite . prior to this study, research on mcqs generation using llms primarily focused on two areas: generating questions and textual options based on textual input @xcite , and incorporating multimodal inputs to generate questions and textual options enriched with visual information @xcite @xcite . however, the task of generating mcqs with visual options remains largely unexplored. according to mayer's cognitive theory of multimedia learning @xcite , visual stimuli play an indispensable role in promoting learners' cognitive engagement by facilitating dual-channel processing, reducing extraneous cognitive load, and enhancing the integration of new information with prior knowledge. there are three major challenges in generating mcqs with visual options. firstly, not all mcq options are suitable for visual representation @xcite . for example, mathematical computation problems typically rely on precise numerical or symbolic expressions which is difficult to convey through static images. secondly, mcqs with visual option require explicit scaffolding for visual analysis; otherwise, students may incur unnecessary cognitive overload @xcite . lastly, current text-to-image (t2i) models face key challenges in visual option generation: (i) the inherent variability in visual option generation often leads to inaccurate or unrealistic outputs. (ii) the educational domain lacks specialized image repositories to meet the precise visual needs of academic content. as shown in figure 1 , the goal of our work is to generate an appropriate question and visual options from an input consisting of an image, context, and the answer, via processing pipeline. to address these challenges, we propose a novel framework named cross-modal options synthesis (cmos), integrating multimodal chain-of-thought (mcot) reasoning with retrieval-augmented generation (rag) to generate mcqs with visual options. specifically, the framework employs an multimodal large language model (mllm) to encode multimodal content and embeds it into a four-stage mcot architecture that separates content discrimination, question and reason generation, alternative pairs screening, and visual options generation. to improve the quality of visual options, we leverage rag to retrieve similar images from an external educational image database as templates for generation, and then the mllm and the t2i model are required to optimize based on the templates. the practical and impactful contributions of this work in mcqs can be summarized as follows: ## method problem definition given an input consisting of @xmath0, i, a), where t represents the text and i represents the image, and a denotes the answer, the task is to generate an output that includes a highquality question q, a visual option a ′ corresponding to the answer, and multiple visual distractors d s , each associated with a textual distractor. ## conclusion in this paper, we present a novel framework called cross-modal options synthesis (cmos), which combines retrieved similar exemplars and multimodal chain-of-thought (mcot) reasoning to generate educational multiple-choice questions and visual options from multimodal input. specifically, we utilize mllms to encode multimodal contexts and incorporate them into a three-stage multimodal-cot framework, namely content discrimination, question generation, and visual option generation. meanwhile, we introduce a similar exemplar retrieval module to guide the discrimination. what's more, we use oqrm module to select optimal question and reasoning progress. finally, we leverage template-based and slight tuning strategies to generate educational visual options. our experimental results on three test sets demonstrate that cmos outperforms all existing methods and models, achieving new state-of-the-art performance. more importantly, our study highlights the potential of visual-option-based multiple-choice question generation to enhance multimodal teaching resources, support personalized learning, and foster deeper understanding in educational settings.