Navigating Nuance
Navigating Generative Artificial Intelligence Adoption in Higher Education Through Kotter's Change Management Framework
Paper Presentation in a Themed Session Faiq Waghid
Using generative artificial intelligence (AI) has brought several opportunities and problems to higher education. This conceptual study examines how John Kotter's Eight-Step Change Management process can be used to successfully and effectively adopt generative AI. There is a growing need for generative AI to be implemented in higher education because it facilitates individualised learning experiences, optimises operational efficiencies, and encourages creative pedagogy. However, issues with eliminating personal connection, compromising academic integrity, and moral conundrums must be properly thought through. This research uses Kotter's approach to propose a way for higher education institutions to integrate generative AI into their current processes. The model's emphasis on forging a common vision, instilling new behaviours into institutional culture, and generating a sense of urgency guarantees that adopting AI is consistent with ethical norms and educational goals. The study shows how Kotter's methods might help institutions implement AI by using Cape Peninsula University of Technology (CPUT) in Cape Town, South Africa, as an example. By emphasising technological innovation, CPUT shows how AI may be included ethically and inclusively, protecting academic integrity and encouraging cooperation. This study focuses on how ethically responsible higher education institutions can handle the complexities of AI adoption to improve education's quality, accessibility, and inclusivity.
Featured A GPT is a GPT, but can it Equalise? A Quantitative Study of Adoption, Inclusion, and Engagement from Students’ Perceptions and Reported Use of Generative AI Tools
Paper Presentation in a Themed Session Malcolm Roy Weaich, Fatima Rahiman, Greig Krull, Shirra Moch, Fiona MacAlister, Laura Dison
Despite the growing integration of Generative AI (GAI) tools in higher education, disparities in adoption, engagement, and skill development persist among students. These disparities may be influenced (and potentially further exacerbated) by factors such as socioeconomic status (SES), varying levels of institutional support, and differences in faculty practices (Abbasi et al., 2024; Almassaad et al., 2024; Chung, 2015). This study illustrates the roles of perceived usefulness, socioeconomic status, institutional support, and faculty affiliation in shaping students' interactions with GAI tools. Data was collected through an anonymised survey completed by 581 consenting students from five faculties at a research-intensive South African university. Through an explanatory quantitative research strategy, statistical tests (including Spearman Rank Correlation, Mann-Whitney U Test, Kruskal-Wallis H Test, and Chi-Square Test), reveal that perceived usefulness significantly drives engagement (|p| = 0.531, p < 0.001), while socioeconomic disparities impact engagement frequency (p = 0.0449). Institutional support enhances self-efficacy in reading and writing (p = 0.0303), and engagement positively correlates with skill development outcomes such as writing (|p| = 0.2524, p < 0.001). The study was framed by the Adoption, Inclusion and Engagement (AIE) framework developed specifically for this study. Informed by the Technology Acceptance Model, Digital Divide Theory, Self-Efficacy Theory, and underpinned by Socio-Constructivist Learning Theory, the AIE framework highlights the intersection of technology adoption, inclusive practices, and active engagement. Future research should focus on integrating GAI tools into curricula across disciplines, providing guidelines for use, and addressing socioeconomic barriers to access of the tools.
Teaching Data Science and Data Visualization in the AI Era: Best Practices and Implications for Disinformation
Paper Presentation in a Themed Session Raquel Cabero Quiles
Artificial Intelligence (AI) is transforming our society and deeply reshaping the education sector, particularly in fields such as data science and data visualization. These disciplines are experiencing a rapid revolution due to the vast amounts of data generated daily and AI's robust capabilities in automating tasks, enhancing data quality, and improving predictive analytics, thus accelerating the growth and demand for data professionals. The approach to teaching in higher education must evolve and adapt to ensure students are effectively prepared to tackle real-world issues. This research focuses on understanding the nature of data science and data visualization, fields rapidly evolving in the AI digital era. The critical skills, tools, and methodologies students need to acquire to work efficiently and succeed professionally will be analyzed. Furthermore, this proposal addresses the critical challenge of disinformation, which according to the World Economic Forum (WEF), will be the main risk over the next two years, a concern that will remain valid throughout the coming decade. This study examines how institutions should educate students in the previously mentioned fields, providing them with capabilities to identify, analyze, and counteract disinformation. The research employs a qualitative, multi-method approach, including a comprehensive analysis of case studies, expert interviews, policy analysis, and technology evaluation. Finally, this paper provides recommendations to educators and industry leaders on how they should educate students to effectively address today's global challenges in an AI-driven era.
