Exploring Generative AI Policies in Higher Education: A Comparative Perspective from China, Japan, Mongolia, and the USA
- URL: http://arxiv.org/abs/2407.08986v1
- Date: Fri, 12 Jul 2024 04:44:09 GMT
- Title: Exploring Generative AI Policies in Higher Education: A Comparative Perspective from China, Japan, Mongolia, and the USA
- Authors: Qin Xie, Ming Li, Ariunaa Enkhtur,
- Abstract summary: This study conducts a comparative analysis of national policies on Generative AI across four countries: China, Japan, Mongolia, and the USA.
While all four countries exhibit a positive attitude toward Generative AI in higher education, Japan and the USA prioritize a human-centered approach.
China and Mongolia prioritize national security concerns, with their guidelines focusing more on the societal level rather than being specifically tailored to education.
- Score: 6.109371615636878
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study conducts a comparative analysis of national policies on Generative AI across four countries: China, Japan, Mongolia, and the USA. Employing the Qualitative Comparative Analysis (QCA) method, it examines the responses of these nations to Generative AI in higher education settings, scrutinizing the diversity in their approaches within this group. While all four countries exhibit a positive attitude toward Generative AI in higher education, Japan and the USA prioritize a human-centered approach and provide direct guidance in teaching and learning. In contrast, China and Mongolia prioritize national security concerns, with their guidelines focusing more on the societal level rather than being specifically tailored to education. Additionally, despite all four countries emphasizing diversity, equity, and inclusion, they consistently fail to clearly discuss or implement measures to address the digital divide. By offering a comprehensive comparative analysis of attitudes and policies regarding Generative AI in higher education across these countries, this study enriches existing literature and provides policymakers with a global perspective, ensuring that policies in this domain promote inclusion rather than exclusion.
Related papers
- Navigating Governance Paradigms: A Cross-Regional Comparative Study of Generative AI Governance Processes & Principles [19.25514463100802]
This paper aims toDepict the nuances of nascent and diverse governance approaches based on risks, rules, outcomes, principles, or a mix across different regions around the globe.
Our research introduces a Harmonized GenAI Framework, "H-GenAIGF," based on the current governance approaches of six regions: European Union (EU), United States (US), China (CN), Canada (CA), United Kingdom (UK), and Singapore (SG)
arXiv Detail & Related papers (2024-08-14T08:16:44Z) - Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment.
The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment.
We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
arXiv Detail & Related papers (2024-06-13T16:03:25Z) - Comparative Analysis Vision of Worldwide AI Courses [11.231658712906878]
This research delves into the diverse course structures of leading universities, exploring contemporary trends and priorities to reveal the nuanced approaches in AI education.
It also investigates the core AI topics and learning contents frequently taught, comparing them with the CS2023 curriculum guidance to identify convergence and divergence.
arXiv Detail & Related papers (2024-06-04T03:53:57Z) - Responsible Adoption of Generative AI in Higher Education: Developing a "Points to Consider" Approach Based on Faculty Perspectives [0.0]
This paper proposes an approach to the responsible adoption of generative AI in higher education.
It employs a ''points to consider'' approach that is sensitive to the goals, values, and structural features of higher education.
arXiv Detail & Related papers (2024-06-01T23:25:06Z) - Securing the Future of GenAI: Policy and Technology [50.586585729683776]
Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety.
A workshop co-organized by Google, University of Wisconsin, Madison, and Stanford University aimed to bridge this gap between GenAI policy and technology.
This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress?
arXiv Detail & Related papers (2024-05-21T20:30:01Z) - Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines [11.470910427306569]
This study analyzes academic policies and guidelines established by top-ranked U.S. universities regarding the use of GenAI.
Results show that the majority of these universities adopt an open but cautious approach towards GenAI.
Findings provide four practical implications for educators in teaching practices.
arXiv Detail & Related papers (2023-12-08T18:33:11Z) - AI Alignment: A Comprehensive Survey [70.35693485015659]
AI alignment aims to make AI systems behave in line with human intentions and values.
We identify four principles as the key objectives of AI alignment: Robustness, Interpretability, Controllability, and Ethicality.
We decompose current alignment research into two key components: forward alignment and backward alignment.
arXiv Detail & Related papers (2023-10-30T15:52:15Z) - C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for
Foundation Models [58.42279750824907]
We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context.
C-Eval comprises multiple-choice questions across four difficulty levels: middle school, high school, college, and professional.
We conduct a comprehensive evaluation of the most advanced LLMs on C-Eval, including both English- and Chinese-oriented models.
arXiv Detail & Related papers (2023-05-15T03:20:19Z) - A Comprehensive AI Policy Education Framework for University Teaching
and Learning [0.0]
This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies.
Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities.
The study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning.
arXiv Detail & Related papers (2023-04-29T15:35:39Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
Curriculum [58.86139968005518]
The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education.
A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum.
The co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
arXiv Detail & Related papers (2021-01-19T11:26:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.