A Multimodal Multiple-Choice Question Dataset with Expert Difficulty and Bloom's Taxonomy Annotations

Multiple-choice questions (MCQs) are central to educational assessment, where item difficulty plays a key role in test reliability and adaptivity. This article introduces a multimodal dataset of 11,199 MCQs that integrates textual and visual content to support content-based difficulty estimation. Each MCQ consists of a question, four answer options, and an associated image accompanied by an expert-authored caption that describes only the observable visual content, independent of the question or answer options. The dataset includes three forms of expert annotation: (i) visual captions focused exclusively on image content, (ii) categorical difficulty labels assigned using a standardized rubric that considers the overall cognitive effort required to solve the item given its textual and visual components, and (iii) Bloom’s taxonomy–aligned cognitive labels reflecting the intended learning objective of each question. For each image, three MCQs are authored to capture graded difficulty variation while preserving a consistent visual context, spanning topics in the natural and social sciences. Trained secondary-school educators contribute to annotation, verification, and disagreement reconciliation through a structured, multi-phase workflow, ensuring pedagogically grounded and internally consistent labels. Baseline validation checks confirm data integrity and reproducibility. The dataset contains no personal or sensitive information and is released publicly to support research in multimodal reasoning, educational measurement, and intelligent tutoring systems.

This dataset has multiple varieties in it

(a) A multimodal dataset with expert annotated difficulty labels for difficulty estimation.

(b) Also additional subset with noisy annotation of labels and questions for visual modality checking, used for testing model hallucination and other associated tasks.

License: This dataset contains two components with different licenses. The images are sourced from the ScienceQA dataset and are licensed under Creative Commons Attribution–NonCommercial–ShareAlike 4.0 (CC-BY-NC-SA 4.0). All newly created content in this release-including image captions, multiple-choice questions, answer options, Bloom’s-taxonomy labels, difficulty labels, and all associated metadata-is licensed under Creative Commons Attribution–ShareAlike 4.0 (CC-BY-SA 4.0). Any redistribution or reuse that includes the original images must comply with the CC-BY-NC-SA 4.0 terms. The annotation files (questions, captions, labels, and metadata) may be used independently under the more permissive CC-BY-SA 4.0 license, enabling reuse in commercial and non-commercial settings when images are not redistributed.