A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development Through Domain Knowledge Learning in K–12 Settings

计算机科学 生成语法 人工智能 领域(数学分析) 教育技术 主动学习(机器学习) 人机交互 数学教育 心理学 数学分析 数学
作者
Siu Cheung Kong,Yin Yang
出处
期刊:IEEE Transactions on Learning Technologies [Institute of Electrical and Electronics Engineers]
卷期号:17: 1588-1599 被引量:58
标识
DOI:10.1109/tlt.2024.3392830
摘要

The advent of generative artificial intelligence (AI) has ignited an increase in discussions about generative AI tools in education. In this study, a human-centred learning and teaching framework (HCLTF) that uses generative AI tools for self-regulated learning development through domain knowledge learning was proposed to catalyse changes in educational practices. The framework illustrates how generative AI tools can revolutionise educational practices and transform the processes of teaching and learning to become human-centred. It emphasises the evolving roles of teachers, who increasingly become skilful facilitators and humanistic storytellers who craft differentiated instructions and attempt to develop students' individualised learning. Drawing upon insights from neuroscience, the framework guides students to employ generative AI tools to augment their attentiveness, stimulate active engagement in learning, receive immediate feedback, and encourage self-reflection. The pedagogical approach is also reimagined; teachers equipped with generative AI tools and AI literacy can refine their teaching strategies to better equip students to meet future challenges. The practical application of the framework is demonstrated in a case study involving the development of Chinese language writing ability among primary students within a K–12 educational context. This paper also reports the results of a 60-hour development programme for teachers. Specifically, providing in-service teachers with cases involving uses of the proposed framework helped them to better understand the generative AI concepts and integrate them into their teaching and learning and increased their perceived ability to design AI-integrated courses that would enhance students' attention, engagement, confidence, and satisfaction.
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