Arun Srinivasa’s research while affiliated with Texas A&M University and other places

The Dual Mesh Control Domain Method (DMCDM), developed by Reddy (J.N. Reddy. “A dual mesh finite domain method for the numerical solution of differential equations." Int J Comput Methods Eng Sci, 20(3):212-228, 2019), is an alternative to the classical weak-form Galerkin finite element method. An advantage of DMCDM is that it combines the interpolation capabilities of the finite element method with the direct use of integral form of the balance laws. Furthermore, it is easily extensible to mixed formulations, resulting simpler than traditional finite element formulations. In this work, we extend DMCDM to the fully finite strain case with plasticity. We introduce a new discretization algorithm for finite strain problems, which includes a mean-dilatation technique to solve the volumetric locking problem. Assessment is supported by five linear and five finite strain benchmark problems, one of them being 3D. Finite strain solutions were found to be stable, exempt from hourglassing, and also locking-free. Results are found to be competitive with classical F-bar and B-bar elements, with a simpler formulation.

This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanical Engineering course. We compared the grading performance of GPT-4o with that of human teaching assistants (TAs) on ten quiz problems from the MEEN 361 course at Texas A&M University, each answered by approximately 225 students. Both the LLM and TAs followed the same instructor-provided rubric to ensure grading consistency. We evaluated performance using Spearman's rank correlation coefficient and Root Mean Square Error (RMSE) to assess the alignment between rankings and the accuracy of scores assigned by GPT-4o and TAs under zero- and few-shot grading settings. In the zero-shot setting, GPT-4o demonstrated a strong correlation with TA grading, with Spearman's rank correlation coefficient exceeding 0.6 in seven out of ten datasets and reaching a high of 0.9387. Our analysis reveals that GPT-4o performs well when grading criteria are straightforward but struggles with nuanced answers, particularly those involving synonyms not present in the rubric. The model also tends to grade more stringently in ambiguous cases compared to human TAs. Overall, ChatGPT shows promise as a tool for grading conceptual questions, offering scalability and consistency.

Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large (>50 enrolled students) courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered: 1) What types of automated assessment systems can be identified using input, output, and processing framework? 2) What are the educational focus and research motivations of studies with automated assessment systems? 3) What are the reported research outcomes in automated assessment systems and the next steps for educational applications? All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.