Author
Listed:
- Hanzi Zhu
(Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
Research Institute of Hangzhou Artificial Intelligence, Zhejiang Normal University, Hangzhou 311231, China)
- Xin Jiang
(Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
College of Education, Zhejiang Normal University, Jinhua 321004, China)
- Xiaolei Zhang
(Academic Affairs Office, Zhejiang Normal University, Jinhua 321004, China)
- Huiying Xu
(Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China)
- Deang Su
(Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China)
- Zhendong Chen
(Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China)
- Xinzhong Zhu
(Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China
College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China)
Abstract
Artificial intelligence (AI) can help students accelerate assignment completion, but it may also foster cognitive outsourcing and learning detached from authentic contexts. This paper presents E3-HOT, a conceptual framework that leverages embodied intelligence to sustain learners’ cognitive agency and higher-order thinking for sustainable learning, aligned with SDG 4 (Sustainable Development Goal 4) and its emphasis on inclusive and equitable quality education and lifelong learning. Using an iterative conceptual synthesis, we distill three embodied pathways—situational embedding, embodied participation, and cognitive creation—and translate them into a practical system design with a three-module E3 core. It includes a virtual–real integrated learning environment for rich scenarios, embodied interaction for action and sensing, and an intelligent core that provides bounded and teacher-controlled support. To facilitate equitable adoption across resource-diverse settings, we specify multi-fidelity enactment options and an auditable set of evidence artifacts for subsequent expert review and future validation studies. We further provide an illustrative university human–AI design project that outlines a week-by-week workflow and corresponding evidence plan, presented as a worked example rather than a report of an implemented study. E3-HOT offers a traceable design-and-evidence blueprint without claiming measured learning gains.
Suggested Citation
Hanzi Zhu & Xin Jiang & Xiaolei Zhang & Huiying Xu & Deang Su & Zhendong Chen & Xinzhong Zhu, 2026.
"Fostering Sustainable Learning via Embodied Intelligence: The E3-HOT Framework for Higher-Order Thinking in the AI Era,"
Sustainability, MDPI, vol. 18(7), pages 1-25, April.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:7:p:3469-:d:1912458
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