Observatorio IA - educación superior

10 Ways Technology Leaders Can Step Up and In to the Generative AI Discussion in Higher Ed
Lance Eaton, Stan Waddell EDUCAUSE (03/10/2023)
The use of generative AI tools on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and staff about how to navigate these new technological waters. In April 2023, we were involved in a panel with students at College Unbound. The conversation —"Generative AI and Higher Education: Disruption, Opportunities, and Challenges"— offered many different highlights, and the students brought rich thoughts, provocative considerations, and smart ideas, reinforcing the fact that discussions around what to do about generative AI (or about anything else, for that matter) are enhanced when students are involved. Toward the end of the panel conversation, Stan asked the students what they thought could be done to help faculty, students, and staff navigate the rise of AI. Essentially, he was curious to hear about the roles that technology and other leaders could fulfill. After thinking about their answers and engaging in further reflection, we came up with ten suggestions for how to step up and in to the generative AI discussion in higher education.
AI-Powered Higher Ed
Philippa Hardman The Learning Science Newsletter (14/09/2023)
This week, I attended a round table discussion at the House of Commons with politicians and experts from across the education sector to feed into UK policy on AI in Higher Education. Unsurprisingly, one of the key areas of concern and discussion was the impact of AI on academic integrity: in a world where AI can write an essay, what does AI mean for what we assess and how we assess it? And how do we respond in the short term? In this week’s blog post I’ll summarise the discussion and share what we agreed would be the most likely new model of assessment in HE in the post-AI world.
First 100 days of ChatGPT at Australian universities
Samuel Fowler, Malgorzata Korolkiewicz, Rebecca Marrone Learning Letters (13/08/2023)
This article examines the potential impact of large language models (LLMs) on higher education, using the integration of ChatGPT in Australian universities as a case study. Drawing on the experience of the first 100 days of integration, the authors conducted a content analysis of university websites and quotes from spokespeople in the media. Despite the potential benefits of LLMs in transforming teaching and learning, early media coverage has primarily focused on the obstacles to their adoption. The authors argue that the lack of official recommendations for Artificial Intelligence (AI) implementation has further impeded progress. Several recommendations for successful AI integration in higher education are proposed to address these challenges. These include developing a clear AI strategy that aligns with institutional goals, investing in infrastructure and staff training, and establishing guidelines for the ethical and transparent use of AI. The importance of involving all stakeholders in the decision-making process to ensure successful adoption is also stressed. This article offers valuable insights for policymakers and university leaders interested in harnessing the potential of AI to improve the quality of education and enhance the student experience.
A comprehensive AI policy education framework for university teaching and learning
Cecilia Ka Yuk Chan International Journal of Educational Technology in Higher Education (07/07/2023)
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.
MLA-CCCC Joint Task Force on Writing and AI Working Paper: Overview of the Issues, Statement of Principles, and Recommendations
Antonio Byrd, Leonardo Flores, David Green, Holly Hassel, Sarah Z. Johnson, Matthew Kirschenbaum, A. Lockett, Elizabeth Mathews Losh, Anna Mills Modern Language Association of America (20/07/2023)
This working paper discusses the risks and benefits of generative AI for teachers and students in writing, literature, and language programs and makes principle-driven recommendations for how educators, administrators, and policy makers can work together to develop ethical, mission-driven policies and support broad development of critical AI literacy
EDUCAUSE QuickPoll Results: Adopting and Adapting to Generative AI in Higher Ed Tech
Mark McCormack EDUCAUSE (17/04/2023)
EDUCAUSE is helping institutional leaders, technology professionals, and other staff address their pressing challenges by sharing existing data and gathering new data from the higher education community. This report is based on an EDUCAUSE QuickPoll. QuickPolls enable us to rapidly gather, analyze, and share input from our community about specific emerging topics.
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