AI Governance in the Context of the EU AI Act: A Bibliometric and Literature Review Approach
Project Overview
The document examines the evolving role of generative AI, particularly tools like ChatGPT, in education, while emphasizing the necessity for effective governance frameworks, as highlighted by the EU AI Act, to mitigate ethical and privacy concerns associated with these technologies. It underscores the transformative potential of generative AI in enhancing learning outcomes and promoting self-regulated learning, particularly in higher education. The findings suggest that generative AI can facilitate personalized learning experiences, support educators in curriculum development, and improve student engagement. However, the document also calls attention to the risks inherent in deploying such technologies, including potential biases and privacy issues, necessitating a careful balance between innovation and ethical governance. Overall, the document advocates for a comprehensive approach to integrating generative AI in educational settings, ensuring that advancements contribute positively to learning environments while safeguarding against adverse effects.
Key Applications
Generative AI tools (e.g., ChatGPT) for enhancing learning outcomes and self-regulated learning.
Context: Higher education institutions, targeting students and educators.
Implementation: Integration of generative AI tools in educational settings to support personalized learning experiences and assessments.
Outcomes: Improved student engagement, personalized learning pathways, and enhanced self-regulated learning capabilities.
Challenges: Concerns about academic misconduct, potential hindrance to critical thinking skills, and the need for transparency in assessment processes.
Implementation Barriers
Ethical Concerns
The potential for generative AI to facilitate academic misconduct and hinder critical thinking.
Proposed Solutions: Development of guidelines and frameworks that ensure the ethical use of AI in educational settings, including transparency in assessment processes.
Project Team
Byeong-Je Kim
Researcher
Seunghoo Jeong
Researcher
Bong-Kyung Cho
Researcher
Ji-Bum Chung
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Byeong-Je Kim, Seunghoo Jeong, Bong-Kyung Cho, Ji-Bum Chung
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai