This study proposes an innovative cross-domain governance framework for artificial intelligence in education, adapted from the Context-Risk Matrix originally developed for educational technology and subsequently refined for HR recruitment contexts. Building on our previous comparative analysis of AI governance in Indonesia and Malaysia’s recruitment sectors, we demonstrate how this framework can be reverse-adapted to address critical governance challenges in intelligent educational technologies. The study develops a four-quadrant matrix that classifies educational AI tasks based on pedagogical nuance and learning outcome risk, prescribing differentiated governance strategies ranging from general AI for low-risk administrative tasks to specialized, human-in-the-loop systems for high-risk, high-nuance learning interventions. Through an integrative literature review and comparative case analysis of adaptive learning platforms in Southeast Asian educational institutions, we identify parallel governance challenges between HR recruitment and educational AI, including algorithmic bias in student assessment, lack of transparency in adaptive learning pathways, and ethical concerns in personalized educational interventions. The adapted framework provides educators, policymakers, and educational technology developers with a structured, actionable tool for implementing AI responsibly in diverse educational contexts. This study contributes to both educational technology and organizational AI governance literature by demonstrating the transferability of governance frameworks across domains and offering practical strategies for ethical AI adoption in education, particularly relevant for the Asia-Pacific region’s digital transformation in education and workforce development. © 2026 Copyright held by the owner/author(s)