A Hybrid Transformer-Driven and Embedding-Augmented Framework for Automated Contextual MCQ Generation with Semantic Distractor Engineering and Difficulty-Aware Classification

Abstract excerpt

<title>Abstract</title> <p>This study introduces an unsupervised technique for creating multiple-choice questions (MCQs) and deletions from an AI-related article corpus. The method is trained with an MCQ generator via a new strategy that utilizes natural language processing (NLP) principles to generate high-quality, contextually appropriate questions automatically. The main aim is to enhance learning tests and su...

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• Biological Evolution
• Classification
• Cognition
• Genetic Engineering
• Mutation
• Sex Determination Processes
• Students
• Surveys and Questionnaires

Identifiers and source

Literature Corpus work

73fa83e2-8976-5e02-952e-33af93c9a3db

DOI

10.21203/rs.3.rs-9241853/v1