🤖 AI Summary
This study addresses the challenge of delivering timely, consistent, and high-quality feedback in large-scale higher education courses, where resource constraints often impede instructors’ capacity to provide individualized support. To this end, the authors propose a novel retrieval-augmented generation (RAG) system enhanced with pedagogical agent characteristics, representing the first application of a RAG architecture grounded in instructional logic to automated essay scoring. By integrating rubrics, exemplar essays, and historical feedback, the system generates context-aware scores and formative comments tailored to each student’s writing. Evaluation on a corpus of 701 student essays demonstrates strong alignment with human raters, achieving 94%–99% consistency. The approach significantly enhances the accessibility, consistency, and quality of feedback while effectively supporting students’ self-regulated learning.
📝 Abstract
Providing timely, consistent, and high-quality feedback in large-scale higher education courses remains a persistent challenge, often constrained by instructor workload and resource limitations. This study presents an LLM-powered, agentic assessment system built on a Retrieval-Augmented Generation (RAG) architecture to address these challenges. The system integrates a large language model with a structured retrieval mechanism that accesses rubric criteria, exemplar essays, and instructor feedback to generate contextually grounded grades and formative comments. A mixed-methods evaluation was conducted using 701 student essays, combining quantitative analyses of inter-rater reliability, scoring alignment, and consistency with instructor assessments, alongside qualitative evaluation of feedback quality, pedagogical relevance, and student support. Results demonstrate that the RAG system can produce reliable, rubric-aligned feedback at scale, achieving 94--99% agreement with human evaluators, while also enhancing students'opportunities for self-regulated learning and engagement with assessment criteria. The discussion highlights both pedagogical limitations, including potential constraints on originality and feedback dialogue, and the transformative potential of RAG systems to augment instructors'capabilities, streamline assessment workflows, and support scalable, adaptive learning environments. This research contributes empirical evidence for the application of agentic AI in higher education, offering a scalable and pedagogically informed model for enhancing feedback accessibility, consistency, and quality.