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This special issue focus on underlying research with the use of human-centered AI (Artificial Intelligence), where the new design methods and tools can be leveraged and evaluated, hopes to advance AI research, education, policy, and practice to improve the human condition in education. This special issue intends to advocate an in-depth dialogue between researchers with diverse thoughts, genders, ethnicity, and cultures, as well as across disciplines, leading to a better understanding of human-centered AI. Beneficial interactions between researchers could enhance the adoption of human-centered AI in education. This special issue includes ten papers demonstrating how to augment human intelligence with machine intelligence. The ten papers feature human-centered AI in education, AI in language education, AI in learning analytics, ethical reasoning, AI in the clinical workplace, intelligent education robots, AI risk framework, intelligent course recommendation, education chatbots, and intelligent assessment. Together with the ten papers, we achieve a better understanding of the application of human-centered AI in education.

3D Multi-User Virtual Environments (MUVEs) have increasingly become more practical with faster Internet connections, the high processing capacity of ICT devices, and the readiness of learners. However, the educational potential of these immersive worlds in academic settings is closely linked to the combination of appropriate pedagogical and technical design elements. 3D MUVEs that only offer a more immersive experience for their users run the risk of becoming purely performative platforms if they fail to offer appropriately enhanced pedagogical approaches. We, therefore, invite the academic community to focus more on the pedagogical approaches of 3D MUVEs. In this context, this collection of papers aims to outline pedagogical approaches and implementations based on immersive user experiences in 3D MUVEs. It contributes to this field of education with three distinguished papers ranging from theoretical frameworks of user acceptance to training practices for skill development. The findings and suggestions in these papers will provide valuable insights for the academic community and for practitioners willing to benefit from the affordances of 3D MUVEs for learning purposes.