From admiration to apprehension: the evolving role of generative AI in learning
Purpose This study investigates public sentiment and discourse on YouTube regarding the integration of generative artificial intelligence in education, using the technology acceptance model framework. The research explores themes such as democratized access, personalized learning and ethical implications, offering insights into the perceived role and societal impact of GenAI in educational settings. Design/methodology/approach Leximancer’s Topic Guide is employed to analyse user-generated comments on YouTube, identifying key themes and evolving sentiments over time. This approach enabled a detailed examination of GenAI’s perceived usefulness (PU) and perceived ease of use within educational contexts. Findings The analysis reveals strong appreciation for GenAI’s potential to democratize education and personalize learning experiences, with minimal concerns over usability. However, as discourse evolved, ethical concerns around privacy, job displacement and reduced human interaction emerged, reflecting complex societal considerations. Originality/value This study offers a novel exploration of GenAI’s role in education by capturing unfiltered public perceptions from a broad audience beyond traditional educational stakeholders. Unlike most studies, which focus on educators, administrators and policymakers, this research uses YouTube comments to include perspectives from students, parents and the general public. By analysing these diverse voices through TAM, we reveal a nuanced understanding of GenAI’s perceived impact in real-world educational contexts. This approach broadens the scope of educational technology research, highlighting GenAI’s potential and challenges as experienced by the wider public.
Authors
- Walter Barbieri (ORCID: https://orcid.org/0000-0003-2993-3114)
- Ilker Cingillioglu (ORCID: https://orcid.org/0000-0002-2971-140X)
Institutions
- The University of Adelaide (AU)
Publication Details
- Journal
- International Journal of Information and Learning Technology
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1108/ijilt-11-2024-0262
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00