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John Maurice Gayed on Muck Rack

John Maurice Gayed

As seen in: Taylor & Francis Online
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Articles

Computer Science > Artificial Intelligence

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By John Maurice Gayed, Yixuan Huang
| arxiv.org
This study presents WrAFT, a Writing Assessment and Feedback Tool, that delivers both accurate and reliable scores and effective comprehensive feedback to argumentative essays. WrAFT adopts a modular design by dividing automated writing evaluation (AWE) tasks into scoring, surface-level feedback, and deep-level feedback.
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Effectiveness of large language models in automated evaluation of argumentative essays: finetuning vs. zero-shot prompting

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By Qiao Wang, John Maurice Gayed
| Taylor & Francis Online
Abstract To address the long-standing challenge facing traditional automated writing evaluation (AWE) systems in assessing higher-order thinking, this study built an AWE system for scoring argumentative essays by finetuning the GPT-3.5 Large Language Model. The system’s effectiveness was compared with that of the non-finetuned GPT-3.5 and GPT-4 base models via zero-shot prompting, which involves applying the model to perform tasks without any prior specific training or examples on those tasks.
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