An LLM -Powered Assessment Retrieval-Augmented Generation (RAG) For Higher Education

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.