Despite rapid progress in vision-language and large language models (VLMs and LLMs), their effectiveness for AI-driven educational assessment in real-world, underrepresented classrooms remains largely unexplored. We evaluate state-of-the-art VLMs and LLMs on over 14K handwritten answers from grade-4 classrooms in Indonesia, covering Mathematics and English aligned with the local national curriculum. Unlike prior work on clean digital text, our dataset features naturally curly, diverse handwriting from real classrooms, posing realistic visual and linguistic challenges. Assessment tasks include grading and generating personalized Indonesian feedback guided by rubric-based evaluation. Results show that the VLM struggles with handwriting recognition, causing error propagation in LLM grading, yet LLM feedback remains pedagogically useful despite imperfect visual inputs, revealing limits in personalization and contextual relevance.

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.

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.

Through funding cuts and bumps, integration and resegregation, panics and reforms, world wars and culture wars, American students have consistently learned at least one thing well: how to whip out a No. 2 pencil and mark exam answers on a sheet printed with row after row of bubbles. Whether you are an iPad baby or a Baby Boomer, odds are that you have filled in at least a few, if not a few hundred, of these machine-graded multiple-choice forms. They have long been the key ingredient in an alphabet soup of standardized tests, both national (SAT, ACT, TOEFL, LSAT, GRE) and local (SHSAT, STAAR, WVGSA). And they are used in both $50,000-a-year academies and the most impoverished public schools, where the classic green or blue Scantron answer sheets can accompany daily quizzes in every subject. Machine grading, now synonymous with the brand Scantron the way tissues are with Kleenex, is so popular because it can provide rapid and straightforward results for millions of students.

What do you think of when you think of schools and colleges? A classroom full of students furiously scribbling down notes while a teacher is droning on about a topic which is "very important for your midterms". Exams are a very important and indispensable part of education. They are important milestones in a student's educational journey, and students are understandably stressed about them. In an academic year, students have to give as many as 12 exams per semester, which means up to 24 exams in one year!

It is not a news that India has an acute shortage of teachers at elementary, secondary and even at the higher levels of schools. According to the statistics given by the Human Resource & Development (HRD)Ministry of India in 2016, there is a shortage of 1 million teachers across the country. In case of Universities and Colleges, there is a chronic shortage of faculty and the problem of finding qualified people to fill this gap has become even more complicated. In such a scenario, how can India, a country which has the second largest population in the world would cope-up with the challenges of providing quality education to all. Several education experts are saying that our system needs a revolutionary technological intervention. An intervention that would make it more inclusive and accessible.

"Data is abundant and cheap but knowledge is scarce and expensive." In last few years, the sources of data capturing have evolved overwhelmingly. No longer companies limit themselves to surveys, questionnaire and other traditional forms of data collection. Smartphones, online browsing activity, drones, cameras are the modern form of data collection devices. And, believe me, that data is enormous.