A Comprehensive AI Policy Education Framework for University Teaching and Learning

Cecilia Ka Yuk Chan
4/29/2023
cs.CYcs.AI

No confident code implementation found for this paper. 2 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{chan2023comprehensive,
  title  = {A Comprehensive AI Policy Education Framework for University Teaching and Learning},
  author = {Cecilia Ka Yuk Chan},
  year   = {2023},
  eprint = {2305.00280},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.00280v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2305.00280.svg)](https://codeofpaper.com/paper/2305.00280)

Discussion