Constructive, personalized, and timely feedback is essential to student learning. However, providing such feedback in large classes remains a major challenge. Large language models (LLMs) offer alternatives to support instructors by generating relevant feedback that highlights both student strengths and areas for improvement. Nevertheless, most existing LLM-based feedback systems rely on proprietary APIs and are often tailored to specific tasks, which limits their accessibility, flexibility, and applicability in resource-constrained educational settings. In this study, we investigate the potential of two open-source LLMs (DeepSeek R1 and Qwen 3.5) to support automated feedback generation across three disciplines (e.g., programming assignments, essays, and mathematics problems). We evaluate two prompting strategies (unified and multi-agent) across these domains and use human judgment criteria to assess feedback quality. Through this analysis, we examine the potential of open-source models as practical, scalable alternatives to proprietary API-based systems for developing freely accessible feedback-generation tools. Our results show that a single open-source model can generate useful feedback across diverse domains, though with varying effectiveness. DeepSeek R1 performs better on reasoning-intensive tasks such as mathematics, while Qwen 3.5 works best for holistic tasks such as writing, but both models struggle with programming tasks. We find that model architecture matters more than prompting strategy, and reasoning-optimized models excel in structured domains, while general-purpose models perform better on holistic tasks. Finally, we conclude that a multi-agent approach does not consistently guarantee improved results over a single unified LLM approach.

With the rise and widespread adoption of Large Language Models (LLMs) in recent years, extensive research has been conducted on their applications across various domains. One such domain is education, where a key area of interest for researchers is investigating the implementation and reliability of LLMs in grading student responses. This review paper examines studies on the use of LLMs in grading across six academic sub-fields: educational assessment, essay grading, natural sciences and technology, social sciences and humanities, computer science and engineering, and mathematics. It explores how different LLMs are applied in automated grading, the prompting techniques employed, the effectiveness of LLM-based grading for both structured and open-ended responses, and the patterns observed in grading performance. Additionally, this paper discusses the challenges associated with LLM-based grading systems, such as inconsistencies and the need for human oversight. By synthesizing existing research, this paper provides insights into the current capabilities of LLMs in academic assessment and serves as a foundation for future exploration in this area.