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Article: A comprehensive AI policy education framework for university teaching and learning
Title | A comprehensive AI policy education framework for university teaching and learning |
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Authors | |
Keywords | AI policy framework Artificial intelligence Assessment ChatGPT Ethics |
Issue Date | 7-Jul-2023 |
Publisher | SpringerOpen |
Citation | International Journal of Educational Technology in Higher Education, 2023, v. 20, n. 1 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/330158 |
ISSN | 2023 Impact Factor: 8.6 2023 SCImago Journal Rankings: 2.578 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chan, Cecilia Ka Yuk | - |
dc.date.accessioned | 2023-08-15T03:18:15Z | - |
dc.date.available | 2023-08-15T03:18:15Z | - |
dc.date.issued | 2023-07-07 | - |
dc.identifier.citation | International Journal of Educational Technology in Higher Education, 2023, v. 20, n. 1 | - |
dc.identifier.issn | 2365-9440 | - |
dc.identifier.uri | http://hdl.handle.net/10722/330158 | - |
dc.description.abstract | <p>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.<br></p> | - |
dc.language | eng | - |
dc.publisher | SpringerOpen | - |
dc.relation.ispartof | International Journal of Educational Technology in Higher Education | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | AI policy framework | - |
dc.subject | Artificial intelligence | - |
dc.subject | Assessment | - |
dc.subject | ChatGPT | - |
dc.subject | Ethics | - |
dc.title | A comprehensive AI policy education framework for university teaching and learning | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1186/s41239-023-00408-3 | - |
dc.identifier.scopus | eid_2-s2.0-85164110015 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 1 | - |
dc.identifier.eissn | 2365-9440 | - |
dc.identifier.isi | WOS:001022433100001 | - |
dc.identifier.issnl | 2365-9440 | - |