Takeaways
- School leaders are increasingly using AI to automate administrative tasks such as grading, attendance tracking, and report generation, freeing up valuable time for more strategic responsibilities, though implementation requires careful consideration of ethical implications. (Chen et al. (2020), Tian et al. (2024), Chukwuere (2024))
- AI tools are being effectively used to analyze student performance data to identify at-risk students, predict academic outcomes, and provide personalized learning pathways, though their accuracy depends on data quality and requires human oversight to ensure fairness. (Martin et al. (2024), Tian et al. (2024), Zeide (2019))
- Concerns about data privacy, algorithmic bias, and transparency pose significant risks when implementing AI in educational settings, requiring school leaders to establish clear guidelines and ethical frameworks to protect student information and ensure equitable outcomes. (Dotan et al. (2024), Lamberti (2024), Smuha (2020))
- School districts should address infrastructure limitations and connectivity challenges when implementing AI tools, particularly in underserved areas, to ensure equitable access and prevent AI from exacerbating existing educational inequalities. (Bura & Myakala (2024), Nyaaba et al. (2024), Xie et al. (2024))
- Interdisciplinary collaboration between educators, AI researchers, and data scientists is essential for developing effective AI educational tools that balance automation with human-centric teaching approaches while addressing ethical considerations. (Tian et al. (2024), Tong et al. (2024), Nyaaba et al. (2024))
