A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
Automated grading has become an essential tool in education technology due to
its ability to efficiently assess large volumes of student work, provide
consistent and unbiased evaluations, and deliver immediate feedback to enhance
learning. However, current systems face significant limitations, including the
need for large datasets in few-shot learning methods, a lack of personalized
and actionable feedback, and an overemphasis on benchmark performance rather
than student experience. To address these challenges, we propose a Zero-Shot
Large Language Model (LLM)-Based Automated Assignment Grading (AAG) system.
This framework leverages prompt engineering to evaluate both computational and
explanatory student responses without requiring additional training or
fine-tuning. The AAG system delivers tailored feedback that highlights
individual strengths and areas for improvement, thereby enhancing student
learning outcomes. Our study demonstrates the system's effectiveness through
comprehensive evaluations, including survey responses from higher education
students that indicate significant improvements in motivation, understanding,
and preparedness compared to traditional grading methods. The results validate
the AAG system's potential to transform educational assessment by prioritizing
learning experiences and providing scalable, high-quality feedback.
