Yiming Guo
Papers
1
Total Citations
2
H-Index
1
About
Dr. Yiming Guo is a pioneering researcher at the intersection of artificial intelligence and engineering education, with a primary focus on leveraging large language models (LLMs) for automated assessment and personalized learning. Their most-cited work, "Benchmarking Large Language Models on Homework Assessment in Circuit Analysis" (2025, 2 citations), introduces a novel framework for evaluating LLMs’ ability to grade and provide feedback on complex engineering assignments. This contribution is foundational, demonstrating how models like GPT-4 can interpret circuit diagrams, verify calculations, and offer constructive critiques—tasks traditionally requiring expert human oversight. By establishing rigorous benchmarks, Dr. Guo has opened new pathways for scalable, AI-driven tutoring in STEM fields, potentially reducing instructor workload while enhancing student engagement. Their research not only advances educational technology but also highlights the practical challenges of deploying LLMs in domain-specific contexts, such as handling nuanced errors and maintaining pedagogical standards. Dr. Guo’s work is particularly notable for bridging the gap between cutting-edge AI capabilities and real-world classroom needs, making them a key figure in the ongoing transformation of engineering pedagogy.
Research Focus
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Top Papers
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