Table 3 Decisional capital in... | Emerald Publishing

Table 3

Decisional capital in principals' leadership for AI adoption in schools

Key theme	Pattern	References
Human oversight	Principals act as human overseers of AI, ensuring algorithms support but do not replace human judgment	Kilcoyne (2024), Richardson et al. (2025)
Human oversight	Balancing automation with empathy and contextual sensitivity	Ali et al. (2024), Göçen and Döğer (2025)
Ethical discretion	Making decisions that prioritize student welfare, equity, and inclusivity when using AI tools	Polat et al. (2025), Adams and Thompson (2025)
Ethical discretion	Weighing ethical dilemmas such as privacy, surveillance, and bias in AI systems	Krishnan et al. (2024), Akgun and Greenhow (2022)
Algorithmic judgment	Exercising professional skepticism toward AI recommendations; validating accuracy before action	Meng and Sermsri (2024), Dieterle et al. (2024)
Algorithmic judgment	Recognizing AI's limitations and preventing over-reliance on automated insights	Richardson et al. (2025), Göçen and Döğer (2025)
Adaptive contextualization	Applying professional judgment to tailor AI use to local school needs and cultural contexts	Karakose and Tülübas (2024), Yuni et al. (2025)
Adaptive contextualization	Adapting decisions to balance innovation with tradition and community expectations	Fullan et al. (2024), Halomoan et al. (2024)

Key theme	Pattern	References
Human oversight	Principals act as human overseers of AI, ensuring algorithms support but do not replace human judgment	Kilcoyne (2024), Richardson et al. (2025)
Human oversight	Balancing automation with empathy and contextual sensitivity	Ali et al. (2024), Göçen and Döğer (2025)
Ethical discretion	Making decisions that prioritize student welfare, equity, and inclusivity when using AI tools	Polat et al. (2025), Adams and Thompson (2025)
Ethical discretion	Weighing ethical dilemmas such as privacy, surveillance, and bias in AI systems	Krishnan et al. (2024), Akgun and Greenhow (2022)
Algorithmic judgment	Exercising professional skepticism toward AI recommendations; validating accuracy before action	Meng and Sermsri (2024), Dieterle et al. (2024)
Algorithmic judgment	Recognizing AI's limitations and preventing over-reliance on automated insights	Richardson et al. (2025), Göçen and Döğer (2025)
Adaptive contextualization	Applying professional judgment to tailor AI use to local school needs and cultural contexts	Karakose and Tülübas (2024), Yuni et al. (2025)
Adaptive contextualization	Adapting decisions to balance innovation with tradition and community expectations	Fullan et al. (2024), Halomoan et al. (2024)

Source(s): Authors’ own work