Predicting AI Integration in Educational Leadership: A Proposed Chaid-Based Model of Middle Leaders’ Competencies

Kok Ming Goh; Hyginus Lester Junior  Lee

doi:10.70148/rise.v2i6.4

Authors

Kok Ming Goh Author
Hyginus Lester Junior Lee Author

DOI:

https://doi.org/10.70148/rise.v2i6.4

Keywords:

AI Integration; educational leadership; middle leaders; CHAID prediction model; teacher competencies

Abstract

The rapid integration of artificial intelligence (AI) into education is transforming teaching and learning practices worldwide. However, little is known about the factors that drive AI adoption among middle leaders, who play a critical role in shaping digital transformation within schools. This study develops a CHAID-based prediction model to examine the key competency dimensions influencing AI integration among 31 middle leaders from national schools in Labuan, Malaysia. A structured questionnaire was adapted from the UNESCO AI Competency Framework and the ISTE Standards for Educators, comprising 24 items across six dimensions: AI Knowledge (AIK), AI Integration Practices (AIP), AI-Based Assessment (AIA), AI Ethics (AIE), Human-Centered Evaluation (HCE), and Professional Engagement (PEN). Descriptive analysis and decision tree modelling were conducted using SPSS Version 27. Results indicate that AI Knowledge, AI-Based Assessment Design, and Peer Collaboration are the strongest predictors of high AI integration, with the model achieving 83.3% classification accuracy. These findings provide actionable insights for policymakers, school leaders, and teacher educators in designing targeted professional development, supporting Malaysia’s Digital Education Blueprint (2013–2025), and advancing evidence-based strategies to scale AI adoption in educational leadership

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