Exploring Generative AI Policies in Higher Education: A Comparative Perspective from China, Japan, Mongolia, and the USA
Project Overview
The document explores the role of generative AI in education by comparing policies in higher education across China, Japan, Mongolia, and the USA, revealing distinct national approaches: Japan and the USA prioritize human-centered methodologies, whereas China and Mongolia place greater emphasis on national security considerations. Key applications of generative AI include improving teaching materials and making education more accessible for students with disabilities, showcasing its potential to enhance learning experiences. However, the document also highlights significant challenges, including inherent biases in AI technologies and the digital divide that can exacerbate inequalities. The findings underscore the urgent need for inclusive and equitable policies that ensure all students have access to the benefits of AI in educational settings, ultimately promoting a more fair and effective learning environment.
Key Applications
Generative AI tools like ChatGPT
Context: Higher education, targeting educators and students
Implementation: Utilization in developing teaching materials, analyzing student data, and enhancing teaching strategies.
Outcomes: Support for educators, improved accessibility for students with disabilities, and enhanced teaching methods.
Challenges: Risks of propagating biases and problematic content.
Implementation Barriers
Policy
Lack of clear policies addressing the digital divide and the incorporation of local cultures and languages in the implementation of generative AI in education.
Proposed Solutions: Policymakers should develop regulations that promote justice and fairness in access to AI technologies and support the incorporation of local cultures and languages in AI educational frameworks.
Project Team
Qin Xie
Researcher
Ming Li
Researcher
Ariunaa Enkhtur
Researcher
Contact Information
For more information about this project or to discuss potential collaboration opportunities, please contact:
Qin Xie
Source Publication: View Original PaperLink opens in a new window