QuizTools: Question Generation with Explanations

Automated generation of multiple-choice questions (MCQs) represents a viable strategy for scalable assessment and instructional support, but current systems exhibit persistent limitations in distractor quality, reasoning validity, and deployment accessibility. Building on our earlier short paper, YMCQ: Reasoning-Enhanced MCQ Generation, this journal article extends that work by providing a more detailed presentation and explanation of the MCQ generation and multi-stage filtering method introduced there. The method integrates reasoning-based answer generation with misconception-aware distractors, using open-source LLMs fine-tuned on publicly available corpora augmented with synthetic explanations for correct answers and distractors. These augmentations enable the generation of MCQs that are structurally valid, semantically coherent, and pedagogically aligned. The extended presentation further details the method and its implementation in QuizTools, a publicly available platform that supports automated MCQ generation from source materials and enables human-in-the-loop refinement. The pairwise performance evaluation of the generated questions was reported in the short paper; this journal article additionally includes an individual-question quality evaluation, analysis of user edits, and a usability study of QuizTools. The models, datasets, and source code, including over 300,000 quiz items with annotated explanations, are publicly available to facilitate reproducibility and further research in educational NLP.