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

Authors
Siu-Cheung Kong, Yin Yang
Date
Publisher
IEEE

The advent of generative artificial intelligence (AI) has ignited an increase in discussions about generative AI tools in education. In this study, a human-centered learning and teaching framework that uses generative AI tools for self-regulated learning development through domain knowledge learning was proposed to catalyze changes in educational practices. The framework illustrates how generative AI tools can revolutionize educational practices and transform the processes of teaching and learning to become human-centered. It emphasizes the evolving roles of teachers, who increasingly become skillful facilitators and humanistic storytellers who craft differentiated instructions and attempt to develop students’ individualized 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 article also reports the results of a 60-h 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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