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Cited by 298Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

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

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Kong, Siu-Cheung, Yang, Yin·2024·IEEE Transactions on Learning Technologies
DOI 10.1109/tlt.2024.3392830
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Abstract

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…

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Crossref · 10.1109/tlt.2024.3392830

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  • Systems integrating dialogue-based interactions, reflective questioning, and adaptive scaffolding can significantly enhance learning depth and transferability.

    “Systems integrating dialogue-based interactions, reflective questioning, and adaptive scaffolding can also significantly enhance learning depth and transferability, supporting the development of durable skills essential for lifelong learning (Cole et al., 2021 ; Kong & Yang, 2024 ).”

    From Socratic Mind: Impact of a Novel GenAI-Powered Formative Assessment Tool on Student Learning and Higher-Order Thinking (2026) · cites this paper as Kong & Yang, 2024

  • Personalization enhances engagement, as AI tools tailor content and feedback to individual learner needs, thereby increasing motivation, self-efficacy, and perceived relevance.

    “Personalization further enhances engagement, as AI tools tailor content and feedback to individual learner needs, thereby increasing motivation, self-efficacy, and perceived relevance (Kong & Yang, 2024 ; Yilmaz & Yilmaz, 2023 ).”

    From Socratic Mind: Impact of a Novel GenAI-Powered Formative Assessment Tool on Student Learning and Higher-Order Thinking (2026) · cites this paper as Kong & Yang, 2024

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Research works printed but not linked: 31

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Metadata

Authors
Kong, S.-C., & Yang, Y.
Affiliations
Education University of Hong Kong (HK)
ORCID
Venue
IEEE Transactions on Learning Technologies
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024
Volume
17
Pages
1562-1573
ISSN
  • 1939-1382 (ISSN-L)
  • 2372-0050
DOI
10.1109/tlt.2024.3392830
OpenAlex
W4395028987
Cited by
298
References
20
Citations by year
2026: 168 · 2025: 106 · 2024: 24
FWCI
120.59
Type
journal article
Language
en
Open access
Licence
Locations
IEEE Transactions on Learning Technologies (publishedVersion, cc-by)
Funders
Research Grants Council, University Grants Committee of the Hong Kong Special Administrative Region, China (EdUHK CB326, CB357)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
generative AI in education, student self-regulation, K-12 education, AI literacy, teacher professional development, Chinese language writing, elementary school students, student engagement, self-reflection, differentiated instruction, individualised learning

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