Human capital in principals' leadership for AI adoption in schools
| Themes | Patterns | References |
|---|---|---|
| AI Proficiency (n = 10) | Understanding AI systems and applications (e.g. personalized learning, adaptive tools, automated assessment) | Akgun and Greenhow (2022), Igbokwe (2023), Yuni et al. (2025) |
| Knowledge of AI-related emerging technologies (e.g. virtual assistants, facial recognition) | Moravec and Martínez-Bravo (2023), Yuni et al. (2025), Kesim et al. (2025), Renta-Davids et al. (2025) | |
| Data literacy and analytics (e.g. predictive analytics, Big Data, assessing data quality) | Adams and Thompson (2025), Bixler and Ceballos (2025), Igbokwe (2023), Meng and Sermsri (2024) | |
| Awareness of algorithmic issues (bias, transparency, human-in-the-loop responsibilities) | Krishnan et al. (2024), Dieterle et al. (2024), Kilcoyne (2024), Richardson et al. (2025), Ali et al. (2024) | |
| Technological–Adaptive Capacity (n = 7) | Building and communicating a long-term vision for AI integration | Howard et al. (2022), Fullan et al. (2024), Yuni et al. (2025) |
| Exercising visionary leadership in digital transformation and redesigning curriculum and strategic planning with AI | Moravec and Martínez-Bravo (2023), Kesim et al. (2025), Karakose and Tülübas (2023, 2024), Halomoan et al. (2024) | |
| Modeling technology use and motivating teachers | Hejres (2022), Abedi and Ametepey (2024) | |
| Techno-Ethical Knowledge (n = 6) | Addressing ethical challenges (privacy, autonomy, surveillance, fairness, discrimination) | Akgun and Greenhow (2022), Polat et al. (2025) |
| Promoting digital rights and responsible AI use | Krishnan et al. (2024), Polat et al. (2025) | |
| Learning Capacity (n = 5) | Engaging in ongoing learning about AI limitations, biases, and ethical implications | Akgun and Greenhow (2022), Göçen and Döğer (2025), Meng and Sermsri (2024) |
| Themes | Patterns | References |
|---|---|---|
| AI Proficiency ( | Understanding AI systems and applications (e.g. personalized learning, adaptive tools, automated assessment) | |
| Knowledge of AI-related emerging technologies (e.g. virtual assistants, facial recognition) | ||
| Data literacy and analytics (e.g. predictive analytics, Big Data, assessing data quality) | ||
| Awareness of algorithmic issues (bias, transparency, human-in-the-loop responsibilities) | ||
| Technological–Adaptive Capacity ( | Building and communicating a long-term vision for AI integration | |
| Exercising visionary leadership in digital transformation and redesigning curriculum and strategic planning with AI | ||
| Modeling technology use and motivating teachers | ||
| Techno-Ethical Knowledge ( | Addressing ethical challenges (privacy, autonomy, surveillance, fairness, discrimination) | |
| Promoting digital rights and responsible AI use | ||
| Learning Capacity ( | Engaging in ongoing learning about AI limitations, biases, and ethical implications |
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