TOWARDS EFFECTIVE ARGUMENTATION: DESIGN AND IMPLEMENTATION OF A GENERATIVE AI-BASED EVALUATION AND FEEDBACK SYSTEM

被引:1
作者
Jho, Hunkoog [1 ]
Ha, Minsu [2 ]
机构
[1] Dankook Univ, Dept Sci Educ, 152 Jukjeon Ro, Yongin, Gyeonggi Do, South Korea
[2] Seoul Natl Univ, Dept Biol Educ, 1 Gwanak Ro, Seoul, South Korea
来源
JOURNAL OF BALTIC SCIENCE EDUCATION | 2024年 / 23卷 / 02期
基金
新加坡国家研究基金会;
关键词
argumentative writing; artificial intelligence; automated assessment; natural language processing; web architecture;
D O I
10.33225/jbse/24.23.280
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across scientific and non-scientific contexts. The findings revealed marked discrepancies in the performance of AI for extracting argument components, with a significant variance between issues of a scientific nature and those that are not. Higher accuracy was noted in identifying claims, data, and qualifiers, as opposed to rebuttals, backing, and warrants. The study illuminated AI's promise for educational applications but also its shortcomings, such as the increased frequency of erroneous element identification when accuracy was low. This highlights the essential need for more in-depth comparative research on models and the further development of AI to enhance its role in supporting argumentation training.
引用
收藏
页码:280 / 291
页数:12
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