A comprehensive AI policy education framework for university teaching and learning
By Cecilia Ka Yuk Chan. International Journal of Educational Technology in Higher Education, 2023
1,149 citations recorded by the source corpus.
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. Proposed AI Ecological Education Policy Framework for university teaching and learning. Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework. Qualitative and quantitative data collected from students, teachers, and staff. Ten key areas identified for planning an AI policy in universities. Students should play an active role in drafting and implementing the policy. Proposed AI Ecological Education Policy Framework for university teaching and learning. Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework. Qualitative and quantitative data collected from students, teachers, and staff. Ten key areas identified for planning an AI policy in universities. Students should play an active role in drafting and implementing the policy.
Subfields: Artificial Intelligence, Health Informatics, Safety Research
View Paper: https://www.semanticscholar.org/paper/781cfd3b72b3bc690b15433cbbc012487d5553dd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Health Informatics
In Safety Research
Citations in the corpus
- The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? (2023, background): “Chan (2023) provides a framework for developing AI policies in education, which outline actions such as ensuring equitable access to AI, clarifying, identifying, and addressing instances of academic misconduct, rethinking assessments and examinations, and monitoring and evaluating the effectiveness…”
- Students’ voices on generative AI: perceptions, benefits, and challenges in higher education (2023, background): “Chan (2023b) suggests an AI Ecological Education Policy Framework to tackle the various implications of AI integration in university teaching and learning with three dimensions: Pedagogical, Governance, and Operational.”
- The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023, background): “In follow-up work, Chan addresses this need by proposing an AI policy framework specifically for higher education [52].”