A Comprehensive AI Policy Education Framework for University Teaching and Learning

TL;DR

This paper develops an AI policy framework for universities, addressing pedagogical, governance, and operational aspects to guide responsible AI integration in higher education.

Contribution

It introduces the AI Ecological Education Policy Framework, a comprehensive model based on empirical data from Hong Kong universities, to guide AI policy in higher education.

Findings

Stakeholders recognize AI's potential to enhance teaching and learning.

Concerns about privacy, security, and accountability are prominent.

The framework provides a structured approach to AI policy implementation.

Abstract

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, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly.