Educators’ and students’ views on the impact of artificial intelligence in higher education
Artificial intelligence (AI) is reshaping higher education, yet educators and students may not interpret its educational implications in the same way. While previous research has examined AI adoption, use, and attitudes, less is known about how these groups understand AI-related change across time. This study explored how educators and students interpret AI in relation to changes they believe had already occurred, changes currently taking place, and changes they expected in the future. An exploratory qualitative design was adopted using an online sentence-completion survey completed by 223 participants from universities in Singapore, including 71 educators and 152 students. Responses were analysed using inductive thematic analysis followed by content analysis to compare patterns across participant groups. Educators and students recognised many of the same developments associated with AI but consistently interpreted their significance through the different responsibilities associated with their institutional roles. Educators primarily framed AI in relation to assessment, academic standards, teaching practice, and institutional responsibility, whereas students emphasised learning support, access to information, efficiency, and the practical organisation of academic work. These role-based patterns remained evident across participants’ reflections on past, current, and future educational change. The findings suggest that interpretations of AI are shaped not only by the technology itself but also by the institutional roles through which educational change is experienced. The study contributes a role-based and temporal perspectives for understanding how AI-related change is interpreted in higher education.
Authors
- Chin‐Siang Ang (ORCID: https://orcid.org/0000-0003-1868-7827)
Institutions
- Nanyang Technological University (SG)
- Academy of Medicine (SG)
- TMC Academy (SG)
Publication Details
- Journal
- Discover Education
- Published
- 2026-09-21
- DOI
- https://doi.org/10.1007/s44217-026-02174-6
- Primary Topic
- E-Learning and Knowledge Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00