Instructors’ perceptions of a large language model framework for generating automated multiple-choice questions from video transcripts

A novel framework has been developed that leverages Large Language Models (LLMs) to automate the generation of multiple-choice questions (MCQs) from video transcripts. This framework specifically integrates an Item-Writing Flaws (IWF) checklist into the prompt engineering process, aiming to produce high-quality, criterion-aligned MCQs directly, rather than relying solely on post-hoc evaluation. This approach addresses the significant challenge instructors face in manually creating effective MCQs for video-based learning.

This development addresses operational inefficiencies in digital education by automating a time-consuming but critical task: the creation of assessment materials. It has the potential to scale quality content generation, directly impacting the effectiveness and accessibility of video-based learning and supporting consistent pedagogical standards across educational platforms.

Manual creation of high-quality Multiple-Choice Questions (MCQs) for video-based learning is a significant challenge for instructors.

Forward consideration, not a verified fact.