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Bihao Hu; Longwei Zheng; Jiayi Zhu; Lishan Ding; Yilei Wang; Xiaoqing Gu – IEEE Transactions on Learning Technologies, 2024
This study explores and analyzes the specific performance of large language models (LLMs) in instructional design, aiming to unveil their potential strengths and possible weaknesses. Recently, the influence of LLMs has gradually increased in multiple fields, yet exploratory research on their application in education remains relatively scarce. In…
Descriptors: Artificial Intelligence, Natural Language Processing, Instructional Design, Prompting
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Gyeong-Geon Lee; Xiaoming Zhai – IEEE Transactions on Learning Technologies, 2024
While ongoing efforts have continuously emphasized the integration of ChatGPT with science teaching and learning, there are limited empirical studies exploring its actual utility in the classroom. This study aims to fill this gap by analyzing the lesson plans developed by 29 pre-service elementary teachers and assessing how they integrated ChatGPT…
Descriptors: Artificial Intelligence, Natural Language Processing, Science Education, Preservice Teachers