Pritam Sil

Pritam Sil, Parag Chaudhuri, Bhaskaran Raman

With recent advancements in artificial intelligence (AI), there has been growing interest in using state of the art (SOTA) AI solutions to provide assistance in grading handwritten answer sheets. While a few commercial products exist, the question of whether AI-assistance can actually reduce grading effort and time has not yet been carefully considered in published literature. This work introduces an AI-assisted grading pipeline. The pipeline first uses text detection to automatically detect question regions present in a question paper PDF. Next, it uses SOTA text detection methods to highlight important keywords present in the handwritten answer regions of scanned answer sheets to assist in the grading process. We then evaluate a prototype implementation of the AI-assisted grading pipeline deployed on an existing e-learning management platform. The evaluation involves a total of 5 different real-life examinations across 4 different courses at a reputed institute; it consists of a total of 42 questions, 17 graders, and 468 submissions. We log and analyze the grading time for each handwritten answer while using AI assistance and without it. Our evaluations have shown that, on average, the graders take 31% less time while grading a single response and 33% less grading time while grading a single answer sheet using AI assistance.

Pritam Sil, Pushpak Bhattacharyya

Assessments play a vital role in a student's learning process. This is because they provide valuable feedback crucial to a student's growth. Such assessments contain questions with open-ended responses, which are difficult to grade at scale. These responses often require students to express their understanding through textual and visual elements together as a unit. In order to develop scalable assessment tools for such questions, one needs multimodal LLMs having strong comparative reasoning capabilities across multiple modalities. Thus, to facilitate research in this area, we propose the Multimodal Short Answer grading with Feedback (MMSAF) problem along with a dataset of 2,197 data points. Additionally, we provide an automated framework for generating such datasets. As per our evaluations, existing Multimodal Large Language Models (MLLMs) could predict whether an answer is correct, incorrect or partially correct with an accuracy of 55%. Similarly, they could predict whether the image provided in the student's answer is relevant or not with an accuracy of 75%. As per human experts, Pixtral was more aligned towards human judgement and values for biology and ChatGPT for physics and chemistry and achieved a score of 4 or more out of 5 in most parameters.

Dishank Aggarwal, Pritam Sil, Bhaskaran Raman et al.

In recent years, there has been a growing interest in using Artificial Intelligence (AI) to automate student assessment in education. Among different types of assessments, summative assessments play a crucial role in evaluating a student's understanding level of a course. Such examinations often involve short-answer questions. However, grading these responses and providing meaningful feedback manually at scale is both time-consuming and labor-intensive. Feedback is particularly important, as it helps students recognize their strengths and areas for improvement. Despite the importance of this task, there is a significant lack of publicly available datasets that support automatic short-answer grading with feedback generation. To address this gap, we introduce Engineering Short Answer Feedback (EngSAF), a dataset designed for automatic short-answer grading with feedback. The dataset covers a diverse range of subjects, questions, and answer patterns from multiple engineering domains and contains ~5.8k data points. We incorporate feedback into our dataset by leveraging the generative capabilities of state-of-the-art large language models (LLMs) using our Label-Aware Synthetic Feedback Generation (LASFG) strategy. This paper underscores the importance of enhanced feedback in practical educational settings, outlines dataset annotation and feedback generation processes, conducts a thorough EngSAF analysis, and provides different LLMs-based zero-shot and finetuned baselines for future comparison. The best-performing model (Mistral-7B) achieves an overall accuracy of 75.4% and 58.7% on unseen answers and unseen question test sets, respectively. Additionally, we demonstrate the efficiency and effectiveness of our ASAG system through its deployment in a real-world end-semester exam at a reputed institute.