School leaders’ expectations of AI’s impact on students and teachers
Insights from Icils 2023
Bibliographic Data
| ID | 21784427 |
|---|---|
| Authors | Nurullah Eryılmaz (0000-0003-1916-8295), Dana-Kristin Mah (0009-0004-2106-2216), Mehmet Şükrü Bellibaş (0000-0003-1281-4493), Michael Pietsch (0000-0002-9836-6793, corresponding author) |
| Year | 2025 |
| Publication date | 2025-12-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational Management Administration & Leadership (JOURNAL) |
| Journal identifiers | ISSN: 1741-1432 • E-ISSN: 1741-1440 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/17411432251401487 |
| OpenAlex | W4417120467 |
| Language | EN |
| Citations received | 2 |
| References cited | 82 |
This study examines how principals perceive the potential benefits and challenges of artificial intelligence (AI) for students’ learning and teachers’ work across 12 countries, utilizing data from the 2023 International Computer and Information Literacy Study (ICILS 2023). We utilized latent network models, which allow for a flexible, network-based understanding of latent constructs, to examine the structural relationships between variables related to principals’ perceptions of AI. The findings revealed that while many school leaders recognize AI's potential to enhance student engagement and support teaching, they also express worries about its impact on academic integrity, teacher workload, and instructional practices. Those who view AI as beneficial for students tend to see similar advantages for teachers, whereas concerns about increased workload often accompany negative perceptions of AI's role in education. These insights emphasize the importance of developing balanced AI integration strategies that optimize benefits while mitigating potential challenges for educators. We suggest that policymakers design professional learning opportunities for school leaders that address both the benefits and effective integration of AI in teaching and learning, as well as strategies to mitigate potential negative consequences, such as increased workload or unethical use (e.g. cheating)
Academic achievement · Literacy · Perception · Structural equation modeling · Survey data collection · Technology integration · Workload · Educational Assessment and Improvement · Ethics and Social Impacts of AI · Online Learning and Analytics
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| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |