The Intelligent Classroom: How AI and Data are Personalizing Global Education

Background: The rapid integration of Artificial Intelligence (AI) and learning analytics into global education systems is shifting paradigms from standardized instruction toward data-driven personalized learning. However, the practical adoption and ethical implementation of these tools remain highly uneven across different regions. Objective: This study aims to investigate global educator adoption rates, perceived impacts on learning, and the critical governance and equity barriers surrounding AI-driven personalization in the "intelligent classroom." Methods: A quantitative, cross-sectional survey was administered to 120 educators across 12 countries. The survey measured AI usage, perceived impact, and institutional support using a 5-point Likert scale. After excluding incomplete responses and reverse-coding negatively phrased items, the data were analyzed using descriptive statistics, Pearson correlations, and multiple linear regression to predict perceived educational impact. Results: Findings reveal a moderate adoption rate (M = 3.1), heavily mediated by the educator’s level of formal AI training. Educators generally agree that AI improves student engagement and saves planning time. However, a significant governance gap exists, despite moderate usage of generative AI tools; very few educators report having clear institutional policies. Consequently, data overload, algorithmic bias, and student privacy emerged as primary barriers to implementation. Conclusion: The intelligent classroom is ultimately a human-capital and governance challenge, not merely a technological one. To ensure equitable and ethical personalization, policymakers must prioritize robust teacher professional development, establish transparent data privacy frameworks, and guarantee reliable connectivity.