Automated Assessment of Multimodal Answer Sheets in the STEM domain

In the domain of education, the integration of,technology has led to a transformative era, reshaping traditional,learning paradigms. Central to this evolution is the automation,of grading processes, particularly within the STEM domain encompassing Science, Technology, Engineering, and Mathematics.,While efforts to automate grading have been made in subjects,like Literature, the multifaceted nature of STEM assessments,presents unique challenges, ranging from quantitative analysis,to the interpretation of handwritten diagrams. To address these,challenges, this research endeavors to develop efficient and reliable grading methods through the implementation of automated,assessment techniques using Artificial Intelligence (AI). Our,contributions lie in two key areas: firstly, the development of a,robust system for evaluating textual answers in STEM, leveraging,sample answers for precise comparison and grading, enabled by,advanced algorithms and natural language processing techniques.,Secondly, a focus on enhancing diagram evaluation, particularly,flowcharts, within the STEM context, by transforming diagrams,into textual representations for nuanced assessment using a,Large Language Model (LLM). By bridging the gap between,visual representation and semantic meaning, our approach ensures accurate evaluation while minimizing manual intervention.,Through the integration of models such as CRAFT for text,extraction and YoloV5 for object detection, coupled with LLMs,like Mistral-7B for textual evaluation, our methodology facilitates,comprehensive assessment of multimodal answer sheets. This,paper provides a detailed account of our methodology, challenges,encountered, results, and implications, emphasizing the potential,of AI-driven approaches in revolutionizing grading practices in,STEM education.