MuFIN: A Framework for Automating Multimodal Feedback Generation using Generative Artificial Intelligence

Written feedback has long been a cornerstone in educational and professional settings, essential for enhancing learning outcomes. However, multimodal feedback-integrating textual, auditory, and visual cues-promises a more engaging and effective learning experience. By leveraging multiple sensory channels, multimodal feedback better accommodates diverse learning preferences and aids in deeper information retention. Despite its potential, creating multimodal feedback poses challenges, including the need for increased time and resources. Recent advancements in generative artificial intelligence (GenAI) offer solutions to automate the feedback process, predominantly focusing on textual feedback. Yet, the application of GenAI in generating multimodal feedback remains largely unexplored. Our study investigates the use of GenAI techniques to generate multimodal feedback, aiming to provide this feedback for large cohorts of learners, thereby enhancing learning experience and engagement. By exploring the potential of GenAI for this purpose, we propose a framework for automating the generation of multimodal feedback, which we name MuFIN.