E44 Teachers’ Perceptions of AI-Enhanced Data-Driven Decision-Making (DDDM) in Schools: A Systematic Review Research Contexts, Designs, and Trends Studies spanned K-12 levels, primarily secondary education, with limited focus on elementary and special education. Most research originated from educational technology and AI in education, with a smaller number addressing STEM or disciplinary teaching. Geographically, the distribution was uneven (see Figure 2). The United States represented the largest share (5 studies), followed by China (3), Germany (2), Taiwan (2), and Estonia (2). Additional single-country studies emerged from Saudi Arabia, South Korea, Nigeria, and Finland, alongside multi-country European collaborations. This concentration in Western and East Asian contexts underscores the need for greater cultural diversity in future research. Figure 2. Global Distribution of Studies on AI-Enhanced DDDM in Education. The 25 reviewed studies employed varied methodologies: 11 used quantitative surveys (e.g., Alsudairy & Eltantawy, 2024), 7 qualitative case studies (e.g., Kim, 2024), and 7 mixed methods (e.g., Cheah & Kim, 2025). Sample sizes varied widely, ranging from small design-based case studies with 8-30 (e.g., Thompson et al., 2025) teachers to large-scale quantitative surveys involving over 1,000 participants (e.g., Michos et al., 2023). Qualitative studies most frequently employed semi-structured interviews, classroom observations, and co-design workshops. Temporally, publication trends (see Figure 3) indicate a sharp increase from 2023 onward, coinciding with the spread of generative AI. Earlier studies (2020-2022) focused on learning analytics and dashboards, whereas recent work explored generative AI's ethical and pedagogical implications. 1 study 2 studies 3 studies 5 studies
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